Gated Clock Routing Minimizing the Switched Capacitance *

Size: px
Start display at page:

Download "Gated Clock Routing Minimizing the Switched Capacitance *"

Transcription

1 Gated Clock Routng Mnmzng the Swtched Capactance * Jaewon Oh and Massoud Pedram Dept. of Electrcal Engneerng Systems Unversty of Southern Calforna Los Angeles, CA 989 Tel: (3) e-mal: [oh, massoud]@zugros.usc.edu Abstract Ths paper presents a zero-skew gated clock routng technque for VLSI crcuts. The gated clock tree has maskng gates at the nternal nodes of the clock tree, whch are selectvely turned on and off by the gate control sgnals durng the actve and dle tmes of the crcut modules to reduce swtched capactance of the clock tree. Ths work extends the work of [4] so as to account for the swtched capactance and the area of the gate control sgnal routng. Varous tradeoffs between power and area for dfferent desgn optons and module actvtes are dscussed and detaled expermental results are presented. Introducton Clock gatng s an effectve way of reducng power dsspaton n dgtal crcuts. In a typcal synchronous crcut, e.g. a general purpose mcroprocessor, only a porton of the crcut s actve at any gven tme. By shuttng down the dle modules n the crcut, we can prevent the crcut consumng unnecessary power. In addton, we can shut down a porton of the clock tree by maskng off the clock at the nternal node of the tree usng an AND-gate. Ths prevents unnecessary swtchng n the clock tree and saves power n the clock tree n addton to the power savngs n the modules. In ths paper, we address an nstance of the gated clock routng problem. In our gated clock tree, we nsert gates mmedately after every nternal node of the clock tree to mnmze the dynamc power consumpton. These gates also serve as buffers and can be szed to adust the phase delay of the clock sgnal. They are turned on and off by the control sgnals generated from a centralzed gate controller. An nstance of the gated clock tree s shown n Fgure, where the snks correspond to the locatons of modules and the Steners are the nternal nodes of the clock tree. A gate n the clock tree must be enabled (.e. the control sgnal s true) whenever any of ts descendant gates are enabled. Ths suggests that the control sgnal of a gate s the OR functon of the control sgnals of ts descendant gates. * Ths research was funded n part by DARPA under contract no. F C67 and by NSF under contract no. MIP Controller source - snks - Steners clock tree edges gate control sgnals Fgure : Gated clock tree In [5], a gated clock tree topology constructon based on module actvty patterns was suggested. The authors used hgh-level synthess nformaton to extract the actvty patterns. However, the routng of the clock tree and the control sgnals, the actual power dsspaton and the area of them were not consdered. In contrast, our method consders all of these. In addton, we propose a method for clock tree constructon based on the nstructon statstcs of the processor. Ths can be extracted from nstructon level smulaton of the processor wth a number of benchmark programs. The nstructon statstcs are used to extract the actvtes of the nodes and the swtchng actvtes of the control sgnals as wll be dscussed n the followng sectons. More precsely, we wll nvestgate how the probablstc nformaton (nstructon statstcs) and the geometrcal nformaton (snk locatons) are used to gude the low power clock routng. The remander of ths paper s organzed as follows. Secton gves the termnology and the precse problem statement. Secton 3 descrbes how the probabltes of the gate control sgnals are calculated. Secton 4 presents the clock tree constructon algorthm. Sectons 5 and 6 show our expermental results and conclusons.

2 . Problem Defnton We assume that the topology of the clock tree s full bnary, that s, every non-leaf node has exactly two chldren. However, the tree s not necessarly a balanced tree (depth of leaf nodes may not be the same). Let T be the rooted clock tree topology. Let {M, M,,M N } be the modules. If there are N modules, there are N nternal nodes. Let {v, v,,v N- } be the nodes of the clock tree where {v, v,,v N } are leaves and the rest are nternal nodes of the tree. Let {e, e,,e N- } be the edges of the tree. We dentfy each pont v, except the root, of the rooted topology T wth edge e, so e connects v to ts parent n T. Let e be the length of edge e. We assume that the controller s located at the center of the chp. The control sgnal routng s a star routng as shown n Fgure. We denote the controller tree as S. We label each edge n the controller tree as EN. Edge EN controls the gate on edge e of the clock tree. The sgnal probablty of EN (probablty that EN s ) s denoted as P(EN ) and the transton probablty of EN (probablty that EN changes logc value per cycle) s denoted as P tr (EN ). Let EN be the length of edge EN.. Swtched Capactance of the c lock tree Consder a clock tree wthout gates. For a partcular edge e, the power dsspaton on the edge e s gven by power ( e ) = c e α f Vdd () where c, α, f, V dd are the unt wre capactance, transton probablty of the clock net, the clock frequency, and the supply voltage, respectvely. For the clock net, α = snce there s one rsng and one fallng edge n every clock cycle. So the above equaton becomes ( e ) c e f Vdd power = Wth the maskng gates, ths power s dsspated only when the control sgnal s on. Thus the power dsspaton n edge e s ( e ) c e P( EN ) f Vdd power = Durng the layout synthess step, V dd and f are fxed parameters, hence we can use the swtched capactance as an exact measure of the power dsspaton. The swtched capactance w(e ) of an edge e s gven by w e ) = c e P( EN ). ( There may be a load capactance assocated wth each node of the clock tree. Includng the node capactance C at node v, the swtched capactance of e s gven by w e ) = ( c e + C ) P( EN ). ( The total swtched capactance n the clock tree s therefore W ( T ) = ( c e + C ) P( EN ). e. Swtched capactance n the c ontroller tree Smlarly, from Equaton (), we can see the swtched capactance of edge EN s w EN) = ( c EN + C ) P ( EN ) ( g tr where C g s the nput capactance of the AND gate. The total swtched capactance n the controller tree s W S = c EN + C ) P ( EN ) ( ) ( EN The obectve of our gated clock routng s to fnd trees T and S so as to mnmze W = W ( T ) + W ( S) subect to zero skew constrants. Notce that the sgnal probablty of EN determnes the swtched capactance n the clock tree whereas ts transton probablty determnes the swtched capactance n the controller tree. 3. Computaton of P(EN ) and P tr (EN ) To calculate W(T) and W(S), we need to compute the sgnal probabltes P(EN ) and the transton probabltes P tr (EN ). Let P(M ) be the probablty that M s actve (.e. M receves the clock sgnal). Suppose v has modules M, M,,M l at the leaves. If any of these modules are actve, then EN must be turned on. Thus P(EN ) s gven by P EN ) = P( M M M ) () ( l To fnd P tr (EN ), we need the module actvaton statstc over consecutve clock cycles. Let AT(M ) be a two-bt actvaton tag whch represents the module actvtes n two consecutve clock cycles. For example, AT(M ) = means that M s dle n the current clock cycle and becomes actve n the next clock cycle. AT(M ) can have four possble values and ther correspondng logc value transtons of EN are shown below.. AT(M ) = (EN stays at ). AT(M ) = (EN makes a to transton) 3. AT(M ) = (EN makes a to transton) 4. AT(M ) = (EN stays at ) g tr

3 Cases and 4 do not cause transton of EN, so we ust need to consder cases and 3 for the computaton of the transton probablty. If Regster Transfer Level (RTL) smulaton s used to fnd these probabltes, a huge number of clock-by-clock module usages have to be recorded. Certanly, the tme complexty wll be very large, especally for general-purpose mcroprocessors. So we propose a method for computng actvtes usng more effcent nstructon level smulaton of the processor and knowledge about the RTL descrpton of the processor. 3. RTL descrpton For smplcty, we assume that the mcroprocessor has four nstructons and sx modules throughout the rest of ths secton. The RTL descrpton of each nstructon tells us what modules are used to execute each nstructon. For example, we may have the followng RTL descrpton of the nstructons. Instructon Used Modules I M, M, M 3, M 5 I M, M 4 I 3 M, M 5, M 6 I 4 M 3, M 4 Table : RTL descrpton of nstructons 3. Instructon stream By smulatng the processor at the nstructon level wth a number of benchmark programs, we can trace the nstructons that are executed. For example, our nstructon stream for clock cycles may be gven as follows. I I I 4 I I 3 I I I I I I 3 I I I 3 I I I I I 4 I From ths, we can fnd any probabltes by scannng the nstructon stream. For example, M appears n I and I, and these two nstructons occur 5 tmes n the stream, so P(M ) = 5/ =.75. If a node v n the clock tree has two leaf nodes M 5 and M 6, any nstructons that use ether of these modules should contrbute to the sgnal probablty of EN. I and I 3 are such nstructons, so P(EN ) = P(M 5 M 6 ) = / =.55. The transton probablty of ths EN s also found by scannng the nstructon stream. We examne every two consecutve nstructons durng the scannng and count the number of transtons of EN ( transton when both M 5 and M 6 are dle n the current clock cycle and any of M 5 and M 6 are actve n the next clock cycle; transton s the reverse of ths). We can see that P tr (EN ) = /9=.56 (there are 9 transtons). However, the nstructon stream can be very long. To get accurate nstructon statstcs, we may need some mllons of nstructons. Because some nstructons are rarely executed, the nstructon stream should be very long to get reasonable probablty value for the rare nstructons. Therefore, the above brute-force method s very expensve. To overcome ths problem, we propose a method that computes all the necessary probabltes from the tables that can be generated by scannng the nstructon stream ust once. 3.3 Table-drven probablty com putaton Instructon Frequency Table (IFT) enlsts the probablty that each nstructon s executed on the average. By scannng the prevous nstructon stream, we have the IFT n Table. Instructon I I I 3 I 4 Probablty Table : Instructon Frequency Table To fnd P(M 5 M 6 ), we smply add the two probabltes of I and I 3 n Table, whch s.55. Any sgnal probablty P(EN ) can be found usng Table and Table wthout rescannng the nstructon stream. It was shown n [4] that the tme complexty of computng ths probablty s O(KL), where K s the total number of nstructons and L s the maxmum number of used modules for any nstructons (K = L = 4 n our example). Instructon Transton-Module Actvaton Table (IMATT) enlsts AT(M ) for every possble combnaton of two consecutve nstructons. In addton, IMATT keeps the probablty that the two nstructons occur n two consecutve clock cycles. By scannng the prevous nstructon stream, we have IMATT n Table 3. Prob. Instr. M M M 3 M 4 M 5 M 6 Trns..57 I I.58 I I.58 I I 3.57 I I 4.84 I I.57 I I.. I 4 I 4 Table 3: Instructon Transton-Module Actvaton Table For example, I I 3 occurs three tmes n the nstructon stream. So ts probablty s 3/9 =.58. Assume that v has leaf nodes M, M,,M l. In order for EN to make a transton, at least one of AT(M k ), k=,,l should be whle the remanng AT(M k )s should be ether or. Lkewse, n order for EN to make a transton, at least one of AT(M k ), k=,,l should be whle the remanng AT(M k )s should be ether or. All other cases force EN to reman at or. Alternatvely, we perform logcal OR over all the AT(M k )s and f ts result s or, we have transtons on EN. For example, f v has leaf nodes M, M 3, M 5 and M 6, I I causes EN to make a transton snce the OR of the four correspondng table entres s. For each row n Table 3, f the correspondng modules actvaton tags cause EN to

4 make a transton, the probablty on that row should be added to the transton probablty of EN. The tme complexty of computng the transton probablty of EN s O(K N), where K s the total number of nstructons and N s the total number of modules. 4. Clock Tree Constructon 4. Delay modelng To estmate the phase delay of the clock tree, we use the Elmore delay model as was used n [6] for zero-skew clock routng. Insertng gates reduces the subtree capactance n the Elmore delay computaton, thereby reducng the phase delay. 4. Mnmum swtched capactan ce heurstc Bottom-up mergng followed by top-down placement method s commonly used n clock routng. In [], the mergng sector s a lne segment wth slope ±, whch represents the possble locatons of the Stener node where ts two subtrees are merged. These mergng sectors are found n bottom-up fashon. The actual locatons of the Stener nodes are determned n top-down fashon (see an example n Fgure ). The nearest-neghbor heurstc of [3] greedly merges two nodes when the geometrc dstance between the correspondng mergng sectors s mnmum. Our method s also greedy, but the mergng sequence s determned by the swtched capactance. source Stener Snk Mergngsectors Fgure : An example of bottom-up mergng sequence Let ms(v ) be the mergng sector of v. Suppose we try to merge (ms(v ), ms(v )) and the root of the merged tree s v k. We can unquely determne e, e such that the zero skew constrant s satsfed. As mentoned before, we assume that the gate controller s located at the center of the chp. Let ths center be CP. To compute the swtched capactance n an edge of the controller tree, we need to estmate the dstance between the gate locaton (locaton of the Stener node) and the CP. Snce we do not know the exact locatons of the Stener nodes durng the bottom-up phase, we approxmate the edge length of the controller tree as the dstance between the CP and the mddle pont of the mergng sector. Let ths dstance be dst(cp, md(ms(v ))). Then the swtched capactance SC after the merge of (ms(v ), ms(v )) s SC( v, v ) = ( c e + ( c + ( c + C ) P( EN ) + ( c dst( CP, md( ms( v )) + C dst( CP, md( ms( v e g )) + C + C ) P( EN ) g ) P tr ) P tr ( EN ( EN When we merge subtrees bottom-up, we merge sectors that result n the smallest swtched capactance as gven n Equaton (3). Detaled algorthm for the clock tree constructon s smlar to [4]. We summarze the algorthm outlne below (SC s short for swtched capactance). PROCEDURE GatedClockRoutng Input: Instructon Stream, RTL descrpton of each nstructon, Snk locatons; Output: Clock Tree Layout wth gates begn scan the nstructon stream and create IFT and ITMAT; fnd P(EN ) and P tr (EN ) for every snk; compute SC between every par of snks; // bottom-up merge repeat pck the par ms(v x ), ms(v y ) whose SC s mnmum create new node v k ; compute P(EN k ) and P tr (EN k ); fnd ms(v k ); remove node v x, v y ; for each remanng node v n determne e k, e n satsfyng zero-skew; compute SC between v k, v n ; end for untl only the root s left // top-down placement place nternal nodes v k wthn each ms(v k ); end PROCEDURE Scannng the nstructon stream and the creatng the tables take O(B), where B s the length of the stream. The second and the thrd statements take O(N (KL + K N)) and O(N ) respectvely. Snce L < N, the combned complexty s O(K N ). The repeat loop terates N tmes and wthn each teraton, the domnatng complexty s the probablty computaton whch takes O(K N). So the overall complexty s O(B + K N ). 4.3 Reducton of Gates Insertng gates at every node of the clock tree may result n large area and ncrease complexty of the control crcut and ) ) (3)

5 the routng of the enable sgnals. Especally, snce the routng of enable sgnals s a star routng, ts area can be bgger than the clock tree routng f we do not reduce the number of gates. There are cases when nsertng gates hardly reduces swtched capactance. We can thnk of three cases when a node does not need a gate.. Actvty of the node s close to,. Swtched capactance of the node s very small, 3. Actvty of the parent node s almost the same as actvty of the node. Case () s obvous snce there s no tme frame durng whch the node can be shut off. In case (), the node's swtched capactance s so small that havng a gate can only reduce swtched capactance margnally. In case (3), there s very lttle ncrease n actvty when we go up from the node to ts parent. In ths case, t s not necessary for both the node and ts parent to have gates. Only the parent wll have a gate, and the resultng swtched capactance s at most slghtly hgher than the case that both nodes have gates. However, these gate removal schemes may remove so many gates n the tree that the phase delay of the clock sgnal may ncrease rapdly. So we ncluded a rule for enforcng a gate nserton regardless of those three schemes whenever the subtree capactance of the node reaches, say C g, where C g s the nput capactance of a gate. 5. Expermental Results We mplemented our algorthm n C++ on a Sun Sparc workstatons. For snk locatons (module locatons) and the snk load capactance, we used the benchmarks r-r5 from [6]. The nstructon stream and the used modules for each nstructon are generated accordng to a probablstc model of the CPU when t executes typcal programs. The benchmark characterstcs are shown n Table 4. The length of the nstructon stream was thousands for all the benchmarks. The average number of used modules per nstructon s about 4% for all the benchmarks (ths can be seen n the column labeled Ave(M(I ))). That s, about 4% of the modules are actve at any gven tme on the average. Bench No. of snks No. of nstr Ave(M (I )) r r r r r Table 4: Benchmark characterstcs for gated clock routng 5. Buffered clock tree vs. gated clock tree Buffered clock tree s a commonly used method n current clock routng. The buffered clock tree s constructed usng the nearest neghbor heurstc and the sze of a buffer s assumed to be half the sze of AND-gates. The comparson among buffered clock tree, gated clock tree and gated clock tree wth gate reducton heurstc s shown n Fgure Swtched Capactance r r r3 r4 r5 Area r r r3 r4 r5 Fgure 3: Comparson among dfferent clock routng methods. Swtched capactance n pf, area n 6 λ As can be seen from the fgure, f the gate reducton heurstc s not appled, the gated clock routng s worse than the conventonal buffered clock routng. The maor overhead n swtched capactance and the area comes from the star routng. After the gate reducton, t consumes about 3% less power than the buffered clock routng. There s stll however an area overhead. 5. Impact of average module act vty Buffered Gated Gate Red. Buffered Gated Gate Red. If the average actvty s too hgh, then there s lttle room for power savngs. The average module actvty vs. swtched capactance s shown n Fgure 4. As the average module actvty ncreases, the power consumpton dfference between the two routng methods dmnshes. Thus the gated clock routng s more effectve when the module actvty s low. Note that the power consumpton of the gated clock tree wll be at least 4% of the ungated clock tree as a result.

6 Gate Red. Buffered Our expermental results showed that there s an optmum number of gates for lowest swtched capactance. Our results help a desgner choose trade-off among the power, area and the complexty of the routng. In ths paper, we assumed a centralzed gate controller. However we are also nvestgatng a dstrbuted controller for reduced star routng area. Ths s llustrated n Fgure 6. Fgure 4: Average module actvty (x-axs) vs. swtched capactance (y-axs) for benchmark r 5.3 Optmum number of gates If there are a lot of gates, then the swtched capactance n the clock tree s reduced, but the swtched capactance and the area of the controller tree s ncreased. On the contrary, f there are too few gates, the swtched capactance n the clock tree wll be ncreased. Intutvely, there wll be an optmum number of gates that mnmzes the total swtched capactance. Ths s shown n Fgure Swtched Capactance gate reducton (%) Fgure 5: Gate reducton vs. swtched capactance and area for benchmark r We controlled the number of gates by gvng dfferent parameters n the gate reducton heurstc. When there are many gates, the controller tree domnates the swtched capactance and the area. As the number of gates s reduced, the swtched capactance n the controller tree s reduced but that of the clock tree s ncreased. In the fgure, the optmum gate reducton for lowest power s at 55%. 6. Concluson 5 5 Controller tree Clock tree Area gate reducton (%) We presented a gated clock routng whch has lower swtched capactance over buffered clock trees. We presented a clock topology generaton heurstc based on the module actvtes and the snk locatons. We proposed a method to fnd the sgnal probablty and the transton probablty of the gate control sgnals from the tables generated from the nstructon stream. (a) Fgure 6: (a) one centralzed controller vs. (b) four dstrbuted controllers Assume that the chp s square and ts sde s of length D. Then the longest edge length of the star tree s D/. Assumng the average edge length s half of ths (D/4), the total routng area s GD/4 where G s the number of gates. If we dvde the chp nto k equal szed parttons (where k s power of two), then each partton has G/k gates and the average edge length s D / 4 k. Therefore the total routng area s G D GD k = k 4 k 4 k As the number of partton ncreases, the star routng area s reduced by a factor of k. Feasblty of the dstrbuted controllers and ther mpact on the desgn complexty of the controller logc n currently under nvestgaton. References [] Mazhar Aldna, José Montero, Srnvas Devadas, Abht Ghosh, Maros Papaefthymou, Precomputaton-Based Sequental Logc Optmzaton for Low Power, IEEE Transactons on VLSI Systems, vol., no. 4, pp , December, 994. [] Kenneth D. Boese and Adrew B. Kahng, Zero-Skew Clock Routng Trees Wth Mnmum Wrelength, Proc. IEEE Internatonal Conference on ASIC, pp , 99. [3] M. Edahro, Mnmum Path-Length Equ-Dstance Routng, Proc. IEEE Asa-Pacfc Conf. on Crcuts and Systems, pp. 4-46, 99. [4] Jaewon Oh and Massoud Pedram, Power Reducton n Mcroprocessor Chps by Gated Clock Routng, to appear n Asa and South Pacfc Desgn Automaton Conference, 998. [5] Gustavo E. Téllez, Amr Farrah, Mad Sarrafzadeh, Actvty Drven Clock Desgn for Low Power Crcuts, Proc. Internatonal Conference on Computer-Aded Desgn, pp. 6-65, 995. [6] R-S Tsay, Exact zero skew, Internatonal Conference on Computer- Aded Desgn, pp , 99. (b)

Clock-Gating and Its Application to Low Power Design of Sequential Circuits

Clock-Gating and Its Application to Low Power Design of Sequential Circuits Clock-Gatng and Its Applcaton to Low Power Desgn of Sequental Crcuts ng WU Department of Electrcal Engneerng-Systems, Unversty of Southern Calforna Los Angeles, CA 989, USA, Phone: (23)74-448 Massoud PEDRAM

More information

Effective Power Optimization combining Placement, Sizing, and Multi-Vt techniques

Effective Power Optimization combining Placement, Sizing, and Multi-Vt techniques Effectve Power Optmzaton combnng Placement, Szng, and Mult-Vt technques Tao Luo, Davd Newmark*, and Davd Z Pan Department of Electrcal and Computer Engneerng, Unversty of Texas at Austn *Advanced Mcro

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Interconnect Optimization for Deep-Submicron and Giga-Hertz ICs

Interconnect Optimization for Deep-Submicron and Giga-Hertz ICs Interconnect Optmzaton for Deep-Submcron and Gga-Hertz ICs Le He http://cadlab.cs.ucla.edu/~hele UCLA Computer Scence Department Los Angeles, CA 90095 Outlne Background and overvew LR-based STIS optmzaton

More information

Statistical Circuit Optimization Considering Device and Interconnect Process Variations

Statistical Circuit Optimization Considering Device and Interconnect Process Variations Statstcal Crcut Optmzaton Consderng Devce and Interconnect Process Varatons I-Jye Ln, Tsu-Yee Lng, and Yao-Wen Chang The Electronc Desgn Automaton Laboratory Department of Electrcal Engneerng Natonal Tawan

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Annexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances

Annexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances ec Annexes Ths Annex frst llustrates a cycle-based move n the dynamc-block generaton tabu search. It then dsplays the characterstcs of the nstance sets, followed by detaled results of the parametercalbraton

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Variability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning

Variability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning Asa and South Pacfc Desgn Automaton Conference 2008 Varablty-Drven Module Selecton wth Jont Desgn Tme Optmzaton and Post-Slcon Tunng Feng Wang, Xaoxa Wu, Yuan Xe The Pennsylvana State Unversty Department

More information

RT Level Power Analysis y. Jianwen Zhu, Poonam Agrawal, Daniel D. Gajski. components [La94]. its simplicity.

RT Level Power Analysis y. Jianwen Zhu, Poonam Agrawal, Daniel D. Gajski. components [La94]. its simplicity. RT Level Power Analyss y Janwen Zhu, Poonam Agrawal, Danel D. Gajsk Department of Informaton and omputer Scence Unversty of alforna, Irvne, A 92717-3425 Abstract Elevatng power estmaton to archtectural

More information

A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS HCMC Unversty of Pedagogy Thong Nguyen Huu et al. A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Thong Nguyen Huu and Hao Tran Van Department of mathematcs-nformaton,

More information

Appendix B: Resampling Algorithms

Appendix B: Resampling Algorithms 407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles

More information

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

Department of Electrical & Electronic Engineeing Imperial College London. E4.20 Digital IC Design. Median Filter Project Specification

Department of Electrical & Electronic Engineeing Imperial College London. E4.20 Digital IC Design. Median Filter Project Specification Desgn Project Specfcaton Medan Flter Department of Electrcal & Electronc Engneeng Imperal College London E4.20 Dgtal IC Desgn Medan Flter Project Specfcaton A medan flter s used to remove nose from a sampled

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

Calculation of time complexity (3%)

Calculation of time complexity (3%) Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add

More information

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence)

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence) /24/27 Prevew Fbonacc Sequence Longest Common Subsequence Dynamc programmng s a method for solvng complex problems by breakng them down nto smpler sub-problems. It s applcable to problems exhbtng the propertes

More information

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1] DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm

More information

A New Design of Multiplier using Modified Booth Algorithm and Reversible Gate Logic

A New Design of Multiplier using Modified Booth Algorithm and Reversible Gate Logic Internatonal Journal of Computer Applcatons Technology and Research A New Desgn of Multpler usng Modfed Booth Algorthm and Reversble Gate Logc K.Nagarjun Department of ECE Vardhaman College of Engneerng,

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition

Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition Sngle-Faclty Schedulng over Long Tme Horzons by Logc-based Benders Decomposton Elvn Coban and J. N. Hooker Tepper School of Busness, Carnege Mellon Unversty ecoban@andrew.cmu.edu, john@hooker.tepper.cmu.edu

More information

Coarse-Grain MTCMOS Sleep

Coarse-Grain MTCMOS Sleep Coarse-Gran MTCMOS Sleep Transstor Szng Usng Delay Budgetng Ehsan Pakbazna and Massoud Pedram Unversty of Southern Calforna Dept. of Electrcal Engneerng DATE-08 Munch, Germany Leakage n CMOS Technology

More information

TOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION

TOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION 1 2 MULTIPLIERLESS FILTER DESIGN Realzaton of flters wthout full-fledged multplers Some sldes based on support materal by W. Wolf for hs book Modern VLSI Desgn, 3 rd edton. Partly based on followng papers:

More information

EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming

EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-

More information

HMMT February 2016 February 20, 2016

HMMT February 2016 February 20, 2016 HMMT February 016 February 0, 016 Combnatorcs 1. For postve ntegers n, let S n be the set of ntegers x such that n dstnct lnes, no three concurrent, can dvde a plane nto x regons (for example, S = {3,

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Queueing Networks II Network Performance

Queueing Networks II Network Performance Queueng Networks II Network Performance Davd Tpper Assocate Professor Graduate Telecommuncatons and Networkng Program Unversty of Pttsburgh Sldes 6 Networks of Queues Many communcaton systems must be modeled

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud Resource Allocaton wth a Budget Constrant for Computng Independent Tasks n the Cloud Wemng Sh and Bo Hong School of Electrcal and Computer Engneerng Georga Insttute of Technology, USA 2nd IEEE Internatonal

More information

( ) = ( ) + ( 0) ) ( )

( ) = ( ) + ( 0) ) ( ) EETOMAGNETI OMPATIBIITY HANDBOOK 1 hapter 9: Transent Behavor n the Tme Doman 9.1 Desgn a crcut usng reasonable values for the components that s capable of provdng a tme delay of 100 ms to a dgtal sgnal.

More information

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING N. Phanthuna 1,2, F. Cheevasuvt 2 and S. Chtwong 2 1 Department of Electrcal Engneerng, Faculty of Engneerng Rajamangala

More information

Reliable Power Delivery for 3D ICs

Reliable Power Delivery for 3D ICs Relable Power Delvery for 3D ICs Pngqang Zhou Je Gu Pulkt Jan Chrs H. Km Sachn S. Sapatnekar Unversty of Mnnesota Power Supply Integrty n 3D Puttng the power n s as mportant as gettng the heat out Hgher

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

Markov Chain Monte Carlo Lecture 6

Markov Chain Monte Carlo Lecture 6 where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways

More information

The Second Anti-Mathima on Game Theory

The Second Anti-Mathima on Game Theory The Second Ant-Mathma on Game Theory Ath. Kehagas December 1 2006 1 Introducton In ths note we wll examne the noton of game equlbrum for three types of games 1. 2-player 2-acton zero-sum games 2. 2-player

More information

The Study of Teaching-learning-based Optimization Algorithm

The Study of Teaching-learning-based Optimization Algorithm Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute

More information

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL The Synchronous 8th-Order Dfferental Attack on 12 Rounds of the Block Cpher HyRAL Yasutaka Igarash, Sej Fukushma, and Tomohro Hachno Kagoshma Unversty, Kagoshma, Japan Emal: {garash, fukushma, hachno}@eee.kagoshma-u.ac.jp

More information

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11)

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11) Gravtatonal Acceleraton: A case of constant acceleraton (approx. hr.) (6/7/11) Introducton The gravtatonal force s one of the fundamental forces of nature. Under the nfluence of ths force all objects havng

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Analysis of Queuing Delay in Multimedia Gateway Call Routing

Analysis of Queuing Delay in Multimedia Gateway Call Routing Analyss of Queung Delay n Multmeda ateway Call Routng Qwe Huang UTtarcom Inc, 33 Wood Ave. outh Iseln, NJ 08830, U..A Errol Lloyd Computer Informaton cences Department, Unv. of Delaware, Newark, DE 976,

More information

Leakage and Dynamic Glitch Power Minimization Using Integer Linear Programming for V th Assignment and Path Balancing

Leakage and Dynamic Glitch Power Minimization Using Integer Linear Programming for V th Assignment and Path Balancing Leakage and Dynamc Gltch Power Mnmzaton Usng Integer Lnear Programmng for V th Assgnment and Path Balancng Yuanln Lu and Vshwan D. Agrawal Auburn Unversty, Department of ECE, Auburn, AL 36849, USA luyuanl@auburn.edu,

More information

A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS

A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS Shervn Haamn A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS INTRODUCTION Increasng computatons n applcatons has led to faster processng. o Use more cores n a chp

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

Maximizing Overlap of Large Primary Sampling Units in Repeated Sampling: A comparison of Ernst s Method with Ohlsson s Method

Maximizing Overlap of Large Primary Sampling Units in Repeated Sampling: A comparison of Ernst s Method with Ohlsson s Method Maxmzng Overlap of Large Prmary Samplng Unts n Repeated Samplng: A comparson of Ernst s Method wth Ohlsson s Method Red Rottach and Padrac Murphy 1 U.S. Census Bureau 4600 Slver Hll Road, Washngton DC

More information

Week 5: Neural Networks

Week 5: Neural Networks Week 5: Neural Networks Instructor: Sergey Levne Neural Networks Summary In the prevous lecture, we saw how we can construct neural networks by extendng logstc regresson. Neural networks consst of multple

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

Chapter - 2. Distribution System Power Flow Analysis

Chapter - 2. Distribution System Power Flow Analysis Chapter - 2 Dstrbuton System Power Flow Analyss CHAPTER - 2 Radal Dstrbuton System Load Flow 2.1 Introducton Load flow s an mportant tool [66] for analyzng electrcal power system network performance. Load

More information

CHAPTER 17 Amortized Analysis

CHAPTER 17 Amortized Analysis CHAPTER 7 Amortzed Analyss In an amortzed analyss, the tme requred to perform a sequence of data structure operatons s averaged over all the operatons performed. It can be used to show that the average

More information

Outline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique

Outline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique Outlne and Readng Dynamc Programmng The General Technque ( 5.3.2) -1 Knapsac Problem ( 5.3.3) Matrx Chan-Product ( 5.3.1) Dynamc Programmng verson 1.4 1 Dynamc Programmng verson 1.4 2 Dynamc Programmng

More information

Fundamental loop-current method using virtual voltage sources technique for special cases

Fundamental loop-current method using virtual voltage sources technique for special cases Fundamental loop-current method usng vrtual voltage sources technque for specal cases George E. Chatzaraks, 1 Marna D. Tortorel 1 and Anastasos D. Tzolas 1 Electrcal and Electroncs Engneerng Departments,

More information

Abstract. The assumptions made for rank computation are as follows. (see Figure 1)

Abstract. The assumptions made for rank computation are as follows. (see Figure 1) A Novel Metrc for Interconnect Archtecture Performance Parthasarath Dasgupta, Andrew B. Kahng, and Swamy Muddu CSE Department, UCSD, La Jolla, CA 92093-0114 ECE Department, UCSD, La Jolla, CA 92093-0407

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

Stanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7

Stanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7 Stanford Unversty CS54: Computatonal Complexty Notes 7 Luca Trevsan January 9, 014 Notes for Lecture 7 1 Approxmate Countng wt an N oracle We complete te proof of te followng result: Teorem 1 For every

More information

Distributed Sleep Transistor Network for Power Reduction

Distributed Sleep Transistor Network for Power Reduction 11.3 Dstrbuted Sleep Transstor Network for Power Reducton Changbo Long ECE Department Unversty of Wsconsn, Madson clong@cae.wsc.edu Le He EE Department UCLA lhe@ee.ucla.edu ABSTRACT Sleep transstors are

More information

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k) ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Amiri s Supply Chain Model. System Engineering b Department of Mathematics and Statistics c Odette School of Business

Amiri s Supply Chain Model. System Engineering b Department of Mathematics and Statistics c Odette School of Business Amr s Supply Chan Model by S. Ashtab a,, R.J. Caron b E. Selvarajah c a Department of Industral Manufacturng System Engneerng b Department of Mathematcs Statstcs c Odette School of Busness Unversty of

More information

An Optimization Model for Routing in Low Earth Orbit Satellite Constellations

An Optimization Model for Routing in Low Earth Orbit Satellite Constellations An Optmzaton Model for Routng n Low Earth Orbt Satellte Constellatons A. Ferrera J. Galter P. Mahey Inra Inra Inra Afonso.Ferrera@sopha.nra.fr Jerome.Galter@nra.fr Phlppe.Mahey@sma.fr G. Mateus A. Olvera

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

Split alignment. Martin C. Frith April 13, 2012

Split alignment. Martin C. Frith April 13, 2012 Splt algnment Martn C. Frth Aprl 13, 2012 1 Introducton Ths document s about algnng a query sequence to a genome, allowng dfferent parts of the query to match dfferent parts of the genome. Here are some

More information

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS Unversty of Oulu Student Laboratory n Physcs Laboratory Exercses n Physcs 1 1 APPEDIX FITTIG A STRAIGHT LIE TO OBSERVATIOS In the physcal measurements we often make a seres of measurements of the dependent

More information

NP-Completeness : Proofs

NP-Completeness : Proofs NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem

More information

G = G 1 + G 2 + G 3 G 2 +G 3 G1 G2 G3. Network (a) Network (b) Network (c) Network (d)

G = G 1 + G 2 + G 3 G 2 +G 3 G1 G2 G3. Network (a) Network (b) Network (c) Network (d) Massachusetts Insttute of Technology Department of Electrcal Engneerng and Computer Scence 6.002 í Electronc Crcuts Homework 2 Soluton Handout F98023 Exercse 21: Determne the conductance of each network

More information

The optimal delay of the second test is therefore approximately 210 hours earlier than =2.

The optimal delay of the second test is therefore approximately 210 hours earlier than =2. THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple

More information

Tracking with Kalman Filter

Tracking with Kalman Filter Trackng wth Kalman Flter Scott T. Acton Vrgna Image and Vdeo Analyss (VIVA), Charles L. Brown Department of Electrcal and Computer Engneerng Department of Bomedcal Engneerng Unversty of Vrgna, Charlottesvlle,

More information

Speeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem

Speeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem H.K. Pathak et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Speedng up Computaton of Scalar Multplcaton n Ellptc Curve Cryptosystem H. K. Pathak Manju Sangh S.o.S n Computer scence

More information

CHAPTER 14 GENERAL PERTURBATION THEORY

CHAPTER 14 GENERAL PERTURBATION THEORY CHAPTER 4 GENERAL PERTURBATION THEORY 4 Introducton A partcle n orbt around a pont mass or a sphercally symmetrc mass dstrbuton s movng n a gravtatonal potental of the form GM / r In ths potental t moves

More information

Lecture 4: Adders. Computer Systems Laboratory Stanford University

Lecture 4: Adders. Computer Systems Laboratory Stanford University Lecture 4: Adders Computer Systems Laboratory Stanford Unversty horowtz@stanford.edu Copyrght 2004 by Mark Horowtz (w/ Fgures from Hgh-Performance Mcroprocessor Desgn IEEE And Fgures from Bora Nkolc 1

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

A Novel, Low-Power Array Multiplier Architecture

A Novel, Low-Power Array Multiplier Architecture A Noel, Low-Power Array Multpler Archtecture by Ronak Bajaj, Saransh Chhabra, Sreehar Veeramachanen, MB Srnas n 9th Internatonal Symposum on Communcaton and Informaton Technology 29 (ISCIT 29) Songdo -

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

Min Cut, Fast Cut, Polynomial Identities

Min Cut, Fast Cut, Polynomial Identities Randomzed Algorthms, Summer 016 Mn Cut, Fast Cut, Polynomal Identtes Instructor: Thomas Kesselhem and Kurt Mehlhorn 1 Mn Cuts n Graphs Lecture (5 pages) Throughout ths secton, G = (V, E) s a mult-graph.

More information

GEMINI GEneric Multimedia INdexIng

GEMINI GEneric Multimedia INdexIng GEMINI GEnerc Multmeda INdexIng Last lecture, LSH http://www.mt.edu/~andon/lsh/ Is there another possble soluton? Do we need to perform ANN? 1 GEnerc Multmeda INdexIng dstance measure Sub-pattern Match

More information

Second Order Analysis

Second Order Analysis Second Order Analyss In the prevous classes we looked at a method that determnes the load correspondng to a state of bfurcaton equlbrum of a perfect frame by egenvalye analyss The system was assumed to

More information

Chapter 8 SCALAR QUANTIZATION

Chapter 8 SCALAR QUANTIZATION Outlne Chapter 8 SCALAR QUANTIZATION Yeuan-Kuen Lee [ CU, CSIE ] 8.1 Overvew 8. Introducton 8.4 Unform Quantzer 8.5 Adaptve Quantzaton 8.6 Nonunform Quantzaton 8.7 Entropy-Coded Quantzaton Ch 8 Scalar

More information

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

More information

Planning and Scheduling to Minimize Makespan & Tardiness. John Hooker Carnegie Mellon University September 2006

Planning and Scheduling to Minimize Makespan & Tardiness. John Hooker Carnegie Mellon University September 2006 Plannng and Schedulng to Mnmze Makespan & ardness John Hooker Carnege Mellon Unversty September 2006 he Problem Gven a set of tasks, each wth a deadlne 2 he Problem Gven a set of tasks, each wth a deadlne

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

18.1 Introduction and Recap

18.1 Introduction and Recap CS787: Advanced Algorthms Scrbe: Pryananda Shenoy and Shjn Kong Lecturer: Shuch Chawla Topc: Streamng Algorthmscontnued) Date: 0/26/2007 We contnue talng about streamng algorthms n ths lecture, ncludng

More information

Some Consequences. Example of Extended Euclidean Algorithm. The Fundamental Theorem of Arithmetic, II. Characterizing the GCD and LCM

Some Consequences. Example of Extended Euclidean Algorithm. The Fundamental Theorem of Arithmetic, II. Characterizing the GCD and LCM Example of Extended Eucldean Algorthm Recall that gcd(84, 33) = gcd(33, 18) = gcd(18, 15) = gcd(15, 3) = gcd(3, 0) = 3 We work backwards to wrte 3 as a lnear combnaton of 84 and 33: 3 = 18 15 [Now 3 s

More information

Estimating Delays. Gate Delay Model. Gate Delay. Effort Delay. Computing Logical Effort. Logical Effort

Estimating Delays. Gate Delay Model. Gate Delay. Effort Delay. Computing Logical Effort. Logical Effort Estmatng Delas Would be nce to have a back of the envelope method for szng gates for speed Logcal Effort ook b Sutherland, Sproull, Harrs Chapter s on our web page Gate Dela Model Frst, normalze a model

More information

Turing Machines (intro)

Turing Machines (intro) CHAPTER 3 The Church-Turng Thess Contents Turng Machnes defntons, examples, Turng-recognzable and Turng-decdable languages Varants of Turng Machne Multtape Turng machnes, non-determnstc Turng Machnes,

More information

find (x): given element x, return the canonical element of the set containing x;

find (x): given element x, return the canonical element of the set containing x; COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:

More information

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

A new construction of 3-separable matrices via an improved decoding of Macula s construction

A new construction of 3-separable matrices via an improved decoding of Macula s construction Dscrete Optmzaton 5 008 700 704 Contents lsts avalable at ScenceDrect Dscrete Optmzaton journal homepage: wwwelsevercom/locate/dsopt A new constructon of 3-separable matrces va an mproved decodng of Macula

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

COS 521: Advanced Algorithms Game Theory and Linear Programming

COS 521: Advanced Algorithms Game Theory and Linear Programming COS 521: Advanced Algorthms Game Theory and Lnear Programmng Moses Charkar February 27, 2013 In these notes, we ntroduce some basc concepts n game theory and lnear programmng (LP). We show a connecton

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information