Logic effort and gate sizing

Size: px
Start display at page:

Download "Logic effort and gate sizing"

Transcription

1 EEN454 Dgtal Integrated rcut Desgn Logc effort and gate szng EEN 454 Introducton hp desgners face a bewlderng arra of choces What s the best crcut topolog for a functon? How man stages of logc gve least dela? How wde should the transstors be? Logcal effort s a method to make these decsons Uses a smple model of dela Allows back-of-the-envelope calculatons Helps make rapd comparsons between alternatves t Emphaszes remarkable smmetres??? EEN

2 Dela n a Logc Gate Epress delas n process-ndependent unt d = d abs τ τ = 3R 2 ps n 80 nm process 40 ps n 0.6 μm process 2 R A 2 Y 2 R 2 Y 2 R 2 2 τ= Inverter FO effort dela (due to load) τ= Inverter parastc dela (due to ts own parastc) EEN Dela n a Logc Gate Epress delas n process-ndependent unt dabs d = τ Dela has two components d = f + p EEN

3 Dela n a Logc Gate Epress delas n process-ndependent unt d = d abs τ Dela has two components d = f + p Effort dela f = gh (a.k.a. stage effort) Agan has two components EEN Dela n a Logc Gate Epress delas n process-ndependent unt d abs d = τ Dela has two components d = f + p Effort dela f = gh (a.k.a. stage effort) Agan has two components g: logcal effort Measures relatve ablt of gate to delver current g for nverter EEN

4 Dela n a Logc Gate Epress delas n process-ndependent unt d = d abs τ Dela has two components d = f + p Effort dela f = gh (a.k.a. stage effort) Agan has two components h: electrcal effort = out / n Rato of output to nput capactance Sometmes called fanout EEN Dela n a Logc Gate Epress delas n process-ndependent unt d = d abs τ Dela has two components d = f + p Parastc dela p Represents dela of gate drvng no load Set b nternal parastc capactance EEN

5 Dela Plots d = f + p = gh + p Normalzed Dela: d nput NAND g = p = d = Inverter g = p = d = Electrcal Effort: h = out / n EEN Dela Plots d = f + p = gh + p What about NOR2? Normalzed Dela: d nput NAND Inverter g = 4/3 p = 2 d = (4/3)h + 2 g = p = d = h + Effort Dela: f 0 Parastc Dela: p Electrcal Effort: h = out / n EEN

6 omputng Logcal Effort DEF: Logcal effort s the rato of the nput capactance of a gate to the nput capactance of an nverter delverng the same output current. Measure from dela vs. fanout plots Or estmate b countng transstor wdths Addtonal assumptons: Matched pull-up/pull-down currents/effectve resstances A 2 Y A B 2 2 A 4 Y 2 B 4 2 Y n = 3 g = 3/3 n = 4 g = 4/3 n = 5 g = 5/3 EEN f = gh? Effort Dela: How Does It Work f abs = R eff out = R eff n ( out / n ) = R eff n h = R eff ( ( n / n,nv ) n,nv ) h = gh n,nv R eff = gh ( n,nv R n,nv )(R eff /R n,nv ) = gh ( n,nv R n,nv ).0 3R = Tau f = gh EEN

7 atalog of Gates Logcal effort of common gates Gate tpe Number of nputs n Inverter NAND 4/3 5/3 6/3 (n+2)/3 NOR 5/3 7/3 9/3 (2n+)/3 Trstate / mu XOR, XNOR 4, 4 6, 2, 6 8, 6, 6, 8 EEN atalog of Gates Parastc dela of common gates In multples of p nv ( ) Internal node parastcs neglected when computng parastc delas Gate tpe Number of nputs n Inverter NAND n NOR n Trstate / mu n XOR, XNOR EEN

8 Eample: Rng Oscllator Estmate the frequenc of an N-stage rng oscllator Logcal Effort: g = Electrcal Effort: h = Parastc Dela: p = Stage Dela: d = Frequenc: f osc = EEN Eample: Rng Oscllator Estmate the frequenc of an N-stage rng oscllator Logcal Effort: g = Electrcal Effort: h = Parastc Dela: p = q Stage Dela: d = 2 Frequenc: f osc = /(2*N*d) = /4N 3 stage rng oscllator n 0.6 μm process has frequenc of ~ 200 MHz EEN

9 Eample: FO4 Inverter Estmate the dela of a fanout-of-4 (FO4) nverter d Logcal Effort: g = Electrcal Effort: h = Parastc Dela: p = Stage Dela: d = EEN Eample: FO4 Inverter Estmate the dela of a fanout-of-4 (FO4) nverter d Logcal Effort: g = Electrcal Effort: h = 4 Parastc Dela: p = The FO4 dela s about Stage Dela: d = ps n 0.6 μm process 60 ps n a 80 nm process f/3 ns n an f μm process EEN

10 Multstage Logc Networks Logcal effort generalzes to multstage networks Path Logcal Effort Path Electrcal Effort Path Effort G = g H = out-path n-path F = f = gh 0 g = h = /0 g 2 = 5/3 h 2 = / g 3 = 4/3 h 3 = z/ z g 4 = h 4 = 20/z 20 EEN Multstage Logc Networks Logcal effort generalzes to multstage networks Path Logcal Effort Path Electrcal Effort Path Effort an we wrte F = GH? G = g H = out path n path F = f = gh EEN

11 Paths that Branch No! onsder paths that branch: (Sze of each gate s characterzed b ts nput pn capactance) G = H = GH = h = h 2 = F = GH? EEN Paths that Branch No! onsder paths that branch: (Sze of each gate s characterzed b ts nput pn capactance) G = H = 90 / 5 = 8 GH = 8 h = (5 +5) / 5 = 6 h 2 = 90 / 5 = F = g g 2 h h 2 = 36 = 2GH EEN

12 Branchng Effort Introduce branchng effort Accounts for branchng between stages n path on path b = B= b + on path off path Now we compute the path effort F = GBH Note: h = BH EEN Multstage Delas Path Effort Dela Path Parastc Dela Path Dela D F = f P= p D = d = D + P F EEN

13 Desgnng Fast rcuts D= d = D + P F Dela s smallest when each stage bears same effort fˆ = gh = F N Thus mnmum dela of N stage path s N D = NF + P Ths s a ke result of logcal effort Fnd fastest possble dela Doesn t requre calculatng gate szes EEN Gate Szes How wde should the gates be for least dela? fˆ = gh= g = n out n g fˆ out Workng backward, appl capactance transformaton to fnd nput capactance of each gate gven load t drves. heck work b verfng nput cap spec s met. EEN

14 Eample: 3-stage path Select gate szes and for least dela from A to B A 8 45 B 45 EEN Eample: 3-stage path A 8 45 B 45 Logcal Effort G = Electrcal Effort H = Branchng Effort B = Path Effort F = Best Stage Effort ˆf = Parastc Dela P = Dela D = EEN

15 Eample: 3-stage path A 8 45 B 45 Logcal Effort G = (4/3)*(5/3)*(5/3) = 00/27 Electrcal Effort H = 45/8 Branchng Effort B = 3 * 2 = 6 Path Effort F = GBH = 25 3 Best Stage Effort f ˆ = F = 5 Parastc Dela P = = 7 Dela D = 3*5 + 7 = 22 = 4.4 FO4 EEN Eample: 3-stage path Work backward for szes = = A 8 45 B 45 EEN

16 Eample: 3-stage path Work backward for szes = 45 * (5/3) / 5 = 5 = (5*2) * (5/3) / 5 = 0 45 A P: 4 N: 4 P: 4 N: 6 P: 2 N: 3 B 45 EEN Best Number of Stages How man stages should a path use? Mnmzng number of stages s not alwas fastest Eample: drve 64-bt datapath wth unt nverter D = Intal Drver Datapath Load N: f: D: EEN

17 Best Number of Stages How man stages should a path use? Mnmzng number of stages s not alwas fastest Eample: drve 64-bt datapath wth unt nverter Intal Drver D = NF /N + P = N(64) /N + N Datapath Load N: f: D: Fastest EEN Dervaton onsder addng nverters to end of path How man gve least dela? N = ( ) Defne best stage effort n D= NF + p + N n p nv D N N N = F ln F + F + pnv = 0 N ρ = F N ( ) p + ρ lnρ = 0 nv Logc Block: n Stages Path Effort F F N N - n Etra Inverters EEN

18 ( ) p + ρ lnρ = 0 nv Best Stage Effort has no closed-form soluton Neglectng parastcs (p nv = 0), we fnd ρ = 2.78 (e) For p nv =, solve numercall for ρ = 3.59 EEN Senstvt Analss How senstve s dela to usng eactl the best number of stages? D(N) /D(N) (ρ=6) (ρ =2.4) N / N 2.4 < ρ < 6 gves dela wthn 5% of optmal We can be slopp! I lke ρ = 4 EEN

19 Eample Ben Btdddle s the memor desgner for the Motorol 68W86, an embedded automotve processor. Help Ben desgn the decoder for a regster fle. Decoder specfcatons: 6 word regster fle Each word s 32 bts wde Each bt presents load of 3 unt-szed transstors True and complementar address nputs A[3:0] Each nput ma drve 0 unt-szed transstors A[3:0] A[3:0] Ben needs to decde: 32 bts How man stages to use? How large should each gate be? 6 Regster Fle How fast can decoder operate? 4:6 Decoder 6 words EEN Number of Stages Decoder effort s manl electrcal and branchng Electrcal Effort: H = Branchng Effort: B = If we neglect logcal effort (assume G = ) Path Effort: F = Number of Stages: N = EEN

20 Number of Stages Decoder effort s manl electrcal and branchng Electrcal Effort: H = (32*3) 3) / 0 = 9.6 Branchng Effort: B = 8 If we neglect logcal effort (assume G = ) Path Effort: F = GBH = 76.8 Number of Stages: N = log 4 F = 3. Tr a 3-stage desgn EEN Gate Szes & Dela Logcal Effort: G = Path Effort: F = Stage Effort: f ˆf = Path Dela: Gate szes: D = z = = A[3] A[3] A[2] A[2] A[] A[] A[0] A[0] z word[0] 96 unts of wordlne capactance z word[5] EEN

21 Gate Szes & Dela Logcal Effort: G = * 6/3 * = 2 Path Effort: F = GBH = 54 /3 Stage Effort: f ˆ = F = Path Dela: D= 3 fˆ = 22. Gate szes: z = 96*/5.36 = 8 = 8*2/5.36 = 6.7 A[3] A[3] A[2] A[2] A[] A[] A[0] A[0] z word[0] 96 unts of wordlne capactance z word[5] EEN omparson ompare man alternatves wth a spreadsheet Desgn N G P D NAND4-INV NAND2-NOR2 2 20/ INV-NAND4-INV NAND4-INV-INV-INV NAND2-NOR2-INV-INV 4 20/ NAND2-INV-NAND2-INV 4 6/ INV-NAND2-INV-NAND2-INV 5 6/ NAND2-INV-NAND2-INV-INV-INV 6 6/ EEN

22 Revew of Defntons Term Stage Path number of stages N logcal effort g G = g out out-path electrcal effort h = H = n n-path on-path + off-path branchng effort b = B = b on-path effort f = gh F = GBH effort dela f D F = f parastc dela p P= p dela d = f + p D = d = DF + P EEN Method of Logcal Effort ) ompute path effort 2) Estmate best number of stages 3) Sketch path wth N stages 4) Estmate least dela 5) Determne best stage effort 6) Fnd gate szes F = GBH N = log 4 F N D = NF + P ˆ N f = F g out n = fˆ EEN

23 Lmts of Logcal Effort hcken and egg problem Need path to compute G But don t know number of stages wthout G Smplstc dela model Neglects nput rse tme effects Interconnect Iteraton requred n desgns wth wre Mamum speed onl Not mnmum area/power for constraned dela EEN Summar of Logcal Effort Logcal effort s useful for thnkng of dela n crcuts Numerc logcal effort characterzes gates NANDs are faster than NORs n MOS Paths are fastest when effort delas are ~4 Path dela s weakl senstve to stages, szes But usng fewer stages doesn t mean faster paths Dela of path s about log 4 F FO4 nverter delas Inverters and NAND2 best for drvng large caps Provdes language for dscussng fast crcuts But requres practce to master EEN

24 Specal ase: ascaded Inverters Smple elegant answer f neglectng parastc delas 2 3 k p: stage rato L sze + = p sze R + =R /p + = p EEN Dela of ascaded Drvers Dela between stage and + R + = p R Total dela from stage to stage k pr + pr pr k- k- + R k L = pr + pr + + pr + R L / p k- = (k-)pr +R /p k- L EEN

25 Mnmum Dela Stage Rato A = (k-) R, B = R L t = A p + B p -k Let dervatve t = 0 A + (-k) B p -k = 0 p k = (k-) B/A = L / p = [ L / ] /k EEN Optmal Number of Stages L = p k k = ln( L / ) / ln p t = k p R = (ln ( L / ) / ln p) p R Dela t reaches mnmum when stage rato: p = e 2.78 EEN

Lecture 7: Multistage Logic Networks. Best Number of Stages

Lecture 7: Multistage Logic Networks. Best Number of Stages Lecture 7: Multstage Logc Networks Multstage Logc Networks (cont. from Lec 06) Examples Readng: Ch. Best Number of Stages How many stages should a path use? Mnmzng number of stages s not always fastest

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

Introduction to CMOS VLSI Design. Lecture 5: Logical Effort. David Harris. Harvey Mudd College Spring Outline

Introduction to CMOS VLSI Design. Lecture 5: Logical Effort. David Harris. Harvey Mudd College Spring Outline Introduction to CMOS VLSI Design Lecture 5: Logical Effort David Harris Harve Mudd College Spring 00 Outline Introduction Dela in a Logic Gate Multistage Logic Networks Choosing the Best Number of Stages

More information

EE 447 VLSI Design. Lecture 5: Logical Effort

EE 447 VLSI Design. Lecture 5: Logical Effort EE 447 VLSI Design Lecture 5: Logical Effort Outline Introduction Delay in a Logic Gate Multistage Logic Networks Choosing the Best Number of Stages Example Summary EE 4475: VLSI Logical Design Effort

More information

Lecture 6: Logical Effort

Lecture 6: Logical Effort Lecture 6: Logical Effort Outline Logical Effort Delay in a Logic Gate Multistage Logic Networks Choosing the Best Number of Stages Example Summary Introduction Chip designers face a bewildering array

More information

ECE429 Introduction to VLSI Design

ECE429 Introduction to VLSI Design ECE429 Introduction to VLSI Design Lecture 5: LOGICAL EFFORT Erdal Oruklu Illinois Institute of Technology Some of these slides have been adapted from the slides provided by David Harris, Harvey Mudd College

More information

Introduction to CMOS VLSI Design. Logical Effort B. Original Lecture by Jay Brockman. University of Notre Dame Fall 2008

Introduction to CMOS VLSI Design. Logical Effort B. Original Lecture by Jay Brockman. University of Notre Dame Fall 2008 Introduction to CMOS VLSI Design Logical Effort Part B Original Lecture b Ja Brockman Universit of Notre Dame Fall 2008 Modified b Peter Kogge Fall 2010,2011,2015, 2018 Based on lecture slides b David

More information

VLSI Design, Fall Logical Effort. Jacob Abraham

VLSI Design, Fall Logical Effort. Jacob Abraham 6. Logical Effort 6. Logical Effort Jacob Abraham Department of Electrical and Computer Engineering The University of Texas at Austin VLSI Design Fall 207 September 20, 207 ECE Department, University of

More information

Logical Effort. Sizing Transistors for Speed. Estimating Delays

Logical Effort. Sizing Transistors for Speed. Estimating Delays Logical Effort Sizing Transistors for Speed Estimating Delays Would be nice to have a back of the envelope method for sizing gates for speed Logical Effort Book by Sutherland, Sproull, Harris Chapter 1

More information

Lecture 8: Combinational Circuit Design

Lecture 8: Combinational Circuit Design Lecture 8: Combinational Circuit Design Mark McDermott Electrical and Computer Engineering The University of Texas at ustin 9/5/8 Verilog to Gates module mux(input s, d0, d, output y); assign y = s? d

More information

Logical Effort: Designing for Speed on the Back of an Envelope David Harris Harvey Mudd College Claremont, CA

Logical Effort: Designing for Speed on the Back of an Envelope David Harris Harvey Mudd College Claremont, CA Logical Effort: Designing for Speed on the Back of an Envelope David Harris David_Harris@hmc.edu Harvey Mudd College Claremont, CA Outline o Introduction o Delay in a Logic Gate o Multi-stage Logic Networks

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

Logical Effort of Higher Valency Adders

Logical Effort of Higher Valency Adders Logcal Effort of gher Valency Adders Davd arrs arvey Mudd College E. Twelfth St. Claremont, CA Davd_arrs@hmc.edu Abstract gher valency parallel prefx adders reduce the number of logc levels at the expense

More information

Lecture 8: Logic Effort and Combinational Circuit Design

Lecture 8: Logic Effort and Combinational Circuit Design Lecture 8: Logic Effort and Combinational Circuit Design Slides courtesy of Deming Chen Slides based on the initial set from David Harris CMOS VLSI Design Outline q Logical Effort q Delay in a Logic Gate

More information

Very Large Scale Integration (VLSI)

Very Large Scale Integration (VLSI) Very Large Scale Integration (VLSI) Lecture 4 Dr. Ahmed H. Madian Ah_madian@hotmail.com Dr. Ahmed H. Madian-VLSI Contents Delay estimation Simple RC model Penfield-Rubenstein Model Logical effort Delay

More information

1.4 Small-signal models of BJT

1.4 Small-signal models of BJT 1.4 Small-sgnal models of J Analog crcuts often operate wth sgnal levels that are small compared to the bas currents and voltages n the crcut. Under ths condton, ncremental or small-sgnal models can be

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

Energy-Delay Space Exploration of Clocked Storage Elements Using Circuit Sizing

Energy-Delay Space Exploration of Clocked Storage Elements Using Circuit Sizing Energy-elay Space Exploraton of locked Storage Elements Usng rcut Szng Mustafa Aktan Svakumar Paramesvaran Joosk Moon Vojn Oklobdzja Electrcal Engneerng Rchardson, TX 75080 e-mal:aktanmus@utdallas.edu

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

VLSI Design I; A. Milenkovic 1

VLSI Design I; A. Milenkovic 1 ourse dmnstraton PE/EE 47, PE 57 VLI esgn I L8: Pass Transstor Logc epartment of Electrcal and omputer Engneerng Unversty of labama n Huntsvlle leksandar Mlenkovc ( www. ece.uah.edu/~mlenka ) www. ece.uah.edu/~mlenka/cpe57-

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

EE 330 Lecture 24. Small Signal Analysis Small Signal Analysis of BJT Amplifier

EE 330 Lecture 24. Small Signal Analysis Small Signal Analysis of BJT Amplifier EE 0 Lecture 4 Small Sgnal Analss Small Sgnal Analss o BJT Ampler Eam Frda March 9 Eam Frda Aprl Revew Sesson or Eam : 6:00 p.m. on Thursda March 8 n Room Sweene 6 Revew rom Last Lecture Comparson o Gans

More information

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering /

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering / Theory and Applcatons of Pattern Recognton 003, Rob Polkar, Rowan Unversty, Glassboro, NJ Lecture 4 Bayes Classfcaton Rule Dept. of Electrcal and Computer Engneerng 0909.40.0 / 0909.504.04 Theory & Applcatons

More information

+ v i F02E2P2 I. Solution (a.) The small-signal transfer function of the stages can be written as, V out (s) V in (s) = g m1 /g m3.

+ v i F02E2P2 I. Solution (a.) The small-signal transfer function of the stages can be written as, V out (s) V in (s) = g m1 /g m3. ECE 6440 Summer 003 Page 1 Homework Assgnment No. 7 s Problem 1 (10 ponts) A fourstage rng oscllator used as the VCO n a PLL s shown. Assume that M1 and M are matched and M3 and M4 are matched. Also assume

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE School of Computer and Communcaton Scences Handout 0 Prncples of Dgtal Communcatons Solutons to Problem Set 4 Mar. 6, 08 Soluton. If H = 0, we have Y = Z Z = Y

More information

Optimization Methods for Engineering Design. Logic-Based. John Hooker. Turkish Operational Research Society. Carnegie Mellon University

Optimization Methods for Engineering Design. Logic-Based. John Hooker. Turkish Operational Research Society. Carnegie Mellon University Logc-Based Optmzaton Methods for Engneerng Desgn John Hooker Carnege Mellon Unerst Turksh Operatonal Research Socet Ankara June 1999 Jont work wth: Srnas Bollapragada General Electrc R&D Omar Ghattas Cl

More information

A REVIEW OF ERROR ANALYSIS

A REVIEW OF ERROR ANALYSIS A REVIEW OF ERROR AALYI EEP Laborator EVE-4860 / MAE-4370 Updated 006 Error Analss In the laborator we measure phscal uanttes. All measurements are subject to some uncertantes. Error analss s the stud

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

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

Lecture 12: Classification

Lecture 12: Classification Lecture : Classfcaton g Dscrmnant functons g The optmal Bayes classfer g Quadratc classfers g Eucldean and Mahalanobs metrcs g K Nearest Neghbor Classfers Intellgent Sensor Systems Rcardo Guterrez-Osuna

More information

Chapter Eight. Review and Summary. Two methods in solid mechanics ---- vectorial methods and energy methods or variational methods

Chapter Eight. Review and Summary. Two methods in solid mechanics ---- vectorial methods and energy methods or variational methods Chapter Eght Energy Method 8. Introducton 8. Stran energy expressons 8.3 Prncpal of statonary potental energy; several degrees of freedom ------ Castglano s frst theorem ---- Examples 8.4 Prncpal of statonary

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

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

Using the estimated penetrances to determine the range of the underlying genetic model in casecontrol

Using the estimated penetrances to determine the range of the underlying genetic model in casecontrol Georgetown Unversty From the SelectedWorks of Mark J Meyer 8 Usng the estmated penetrances to determne the range of the underlyng genetc model n casecontrol desgn Mark J Meyer Neal Jeffres Gang Zheng Avalable

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 umercal Solutons of oundary-value Problems n Os ovember 7, 7 umercal Solutons of oundary- Value Problems n Os Larry aretto Mechancal ngneerng 5 Semnar n ngneerng nalyss ovember 7, 7 Outlne Revew stff equaton

More information

T E C O L O T E R E S E A R C H, I N C.

T E C O L O T E R E S E A R C H, I N C. T E C O L O T E R E S E A R C H, I N C. B rdg n g En g neern g a nd Econo mcs S nce 1973 THE MINIMUM-UNBIASED-PERCENTAGE ERROR (MUPE) METHOD IN CER DEVELOPMENT Thrd Jont Annual ISPA/SCEA Internatonal Conference

More information

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or

More information

Physics 5153 Classical Mechanics. Principle of Virtual Work-1

Physics 5153 Classical Mechanics. Principle of Virtual Work-1 P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal

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

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

: Numerical Analysis Topic 2: Solution of Nonlinear Equations Lectures 5-11:

: Numerical Analysis Topic 2: Solution of Nonlinear Equations Lectures 5-11: 764: Numercal Analyss Topc : Soluton o Nonlnear Equatons Lectures 5-: UIN Malang Read Chapters 5 and 6 o the tetbook 764_Topc Lecture 5 Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton

More information

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016 ME140 - Lnear rcuts - Wnter 16 Fnal, March 16, 2016 Instructons () The exam s open book. You may use your class notes and textbook. You may use a hand calculator wth no communcaton capabltes. () You have

More information

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features Analyss of the Magnetomotve Force of a Three-Phase Wndng wth Concentrated Cols and Dfferent Symmetry Features Deter Gerlng Unversty of Federal Defense Munch, Neubberg, 85579, Germany Emal: Deter.Gerlng@unbw.de

More information

AS-Level Maths: Statistics 1 for Edexcel

AS-Level Maths: Statistics 1 for Edexcel 1 of 6 AS-Level Maths: Statstcs 1 for Edecel S1. Calculatng means and standard devatons Ths con ndcates the slde contans actvtes created n Flash. These actvtes are not edtable. For more detaled nstructons,

More information

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions 5-6 The Fundamental Theorem of Algebra Content Standards N.CN.7 Solve quadratc equatons wth real coeffcents that have comple solutons. N.CN.8 Etend polnomal denttes to the comple numbers. Also N.CN.9,

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

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems )

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems ) Lecture Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods Numercal Methods Bracketng Methods Open Methods Convergence Notatons Root Fndng

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

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

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

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

Measurement and Uncertainties

Measurement and Uncertainties Phs L-L Introducton Measurement and Uncertantes An measurement s uncertan to some degree. No measurng nstrument s calbrated to nfnte precson, nor are an two measurements ever performed under eactl the

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

How Strong Are Weak Patents? Joseph Farrell and Carl Shapiro. Supplementary Material Licensing Probabilistic Patents to Cournot Oligopolists *

How Strong Are Weak Patents? Joseph Farrell and Carl Shapiro. Supplementary Material Licensing Probabilistic Patents to Cournot Oligopolists * How Strong Are Weak Patents? Joseph Farrell and Carl Shapro Supplementary Materal Lcensng Probablstc Patents to Cournot Olgopolsts * September 007 We study here the specal case n whch downstream competton

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

KIRCHHOFF CURRENT LAW

KIRCHHOFF CURRENT LAW KRCHHOFF CURRENT LAW ONE OF THE FUNDAMENTAL CONSERATON PRNCPLES N ELECTRCAL ENGNEERNG CHARGE CANNOT BE CREATED NOR DESTROYED NODES, BRANCHES, LOOPS A NODE CONNECTS SEERAL COMPONENTS. BUT T DOES NOT HOLD

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

More information

EN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st

EN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st EN40: Dynamcs and bratons Homework 4: Work, Energy and Lnear Momentum Due Frday March 1 st School of Engneerng Brown Unversty 1. The fgure (from ths publcaton) shows the energy per unt area requred to

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

55:041 Electronic Circuits

55:041 Electronic Circuits 55:04 Electronc Crcuts Feedback & Stablty Sectons of Chapter 2. Kruger Feedback & Stablty Confguraton of Feedback mplfer Negate feedback β s the feedback transfer functon S o S S o o S S o f S S S S fb

More information

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise. Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the

More information

Numerical Methods Solution of Nonlinear Equations

Numerical Methods Solution of Nonlinear Equations umercal Methods Soluton o onlnear Equatons Lecture Soluton o onlnear Equatons Root Fndng Prolems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods umercal Methods Bracketng Methods Open

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

e i is a random error

e i is a random error Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

ORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp.

ORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp. Clck to Vew Mathcad Document 2011 Knovel Corp. Buldng Structural Desgn. homas P. Magner, P.E. 2011 Parametrc echnology Corp. Chapter 3: Renforced Concrete Slabs and Beams 3.2 Renforced Concrete Beams -

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

PHYSICS - CLUTCH CH 28: INDUCTION AND INDUCTANCE.

PHYSICS - CLUTCH CH 28: INDUCTION AND INDUCTANCE. !! www.clutchprep.com CONCEPT: ELECTROMAGNETIC INDUCTION A col of wre wth a VOLTAGE across each end wll have a current n t - Wre doesn t HAVE to have voltage source, voltage can be INDUCED V Common ways

More information

So far: simple (planar) geometries

So far: simple (planar) geometries Physcs 06 ecture 5 Torque and Angular Momentum as Vectors SJ 7thEd.: Chap. to 3 Rotatonal quanttes as vectors Cross product Torque epressed as a vector Angular momentum defned Angular momentum as a vector

More information

ELECTRONICS. EE 42/100 Lecture 4: Resistive Networks and Nodal Analysis. Rev B 1/25/2012 (9:49PM) Prof. Ali M. Niknejad

ELECTRONICS. EE 42/100 Lecture 4: Resistive Networks and Nodal Analysis. Rev B 1/25/2012 (9:49PM) Prof. Ali M. Niknejad A. M. Nknejad Unversty of Calforna, Berkeley EE 100 / 42 Lecture 4 p. 1/14 EE 42/100 Lecture 4: Resstve Networks and Nodal Analyss ELECTRONICS Rev B 1/25/2012 (9:49PM) Prof. Al M. Nknejad Unversty of Calforna,

More information

CHAPTER 13. Exercises. E13.1 The emitter current is given by the Shockley equation:

CHAPTER 13. Exercises. E13.1 The emitter current is given by the Shockley equation: HPT 3 xercses 3. The emtter current s gen by the Shockley equaton: S exp VT For operaton wth, we hae exp >> S >>, and we can wrte VT S exp VT Solng for, we hae 3. 0 6ln 78.4 mv 0 0.784 5 4.86 V VT ln 4

More information

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. . For P such independent random variables (aka degrees of freedom): 1 =

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. . For P such independent random variables (aka degrees of freedom): 1 = Fall Analss of Epermental Measurements B. Esensten/rev. S. Errede More on : The dstrbuton s the.d.f. for a (normalzed sum of squares of ndependent random varables, each one of whch s dstrbuted as N (,.

More information

p 1 c 2 + p 2 c 2 + p 3 c p m c 2

p 1 c 2 + p 2 c 2 + p 3 c p m c 2 Where to put a faclty? Gven locatons p 1,..., p m n R n of m houses, want to choose a locaton c n R n for the fre staton. Want c to be as close as possble to all the house. We know how to measure dstance

More information

Boise State University Department of Electrical and Computer Engineering ECE 212L Circuit Analysis and Design Lab

Boise State University Department of Electrical and Computer Engineering ECE 212L Circuit Analysis and Design Lab Bose State Unersty Department of Electrcal and omputer Engneerng EE 1L rcut Analyss and Desgn Lab Experment #8: The Integratng and Dfferentatng Op-Amp rcuts 1 Objectes The objectes of ths laboratory experment

More information

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

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

Generative classification models

Generative classification models CS 675 Intro to Machne Learnng Lecture Generatve classfcaton models Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square Data: D { d, d,.., dn} d, Classfcaton represents a dscrete class value Goal: learn

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Introduction to information theory and data compression

Introduction to information theory and data compression Introducton to nformaton theory and data compresson Adel Magra, Emma Gouné, Irène Woo March 8, 207 Ths s the augmented transcrpt of a lecture gven by Luc Devroye on March 9th 207 for a Data Structures

More information

Solutions to Homework 7, Mathematics 1. 1 x. (arccos x) (arccos x) 1

Solutions to Homework 7, Mathematics 1. 1 x. (arccos x) (arccos x) 1 Solutons to Homework 7, Mathematcs 1 Problem 1: a Prove that arccos 1 1 for 1, 1. b* Startng from the defnton of the dervatve, prove that arccos + 1, arccos 1. Hnt: For arccos arccos π + 1, the defnton

More information

SIO 224. m(r) =(ρ(r),k s (r),µ(r))

SIO 224. m(r) =(ρ(r),k s (r),µ(r)) SIO 224 1. A bref look at resoluton analyss Here s some background for the Masters and Gubbns resoluton paper. Global Earth models are usually found teratvely by assumng a startng model and fndng small

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

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

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

EE141. Administrative Stuff

EE141. Administrative Stuff -Spring 2004 Digital Integrated ircuits Lecture 15 Logical Effort Pass Transistor Logic 1 dministrative Stuff First (short) project to be launched next Th. Overall span: 1 week Hardware lab this week Hw

More information

Limited Dependent Variables and Panel Data. Tibor Hanappi

Limited Dependent Variables and Panel Data. Tibor Hanappi Lmted Dependent Varables and Panel Data Tbor Hanapp 30.06.2010 Lmted Dependent Varables Dscrete: Varables that can take onl a countable number of values Censored/Truncated: Data ponts n some specfc range

More information

55:041 Electronic Circuits

55:041 Electronic Circuits 55:04 Electronc Crcuts Feedback & Stablty Sectons of Chapter 2. Kruger Feedback & Stablty Confguraton of Feedback mplfer S o S ε S o ( S β S ) o Negate feedback S S o + β β s the feedback transfer functon

More information

Pattern Classification

Pattern Classification Pattern Classfcaton All materals n these sldes ere taken from Pattern Classfcaton (nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wley & Sons, 000 th the permsson of the authors and the publsher

More information

Flyback Converter in DCM

Flyback Converter in DCM Flyback Converter n CM m 1:n V O V S m I M m 1 1 V CCM: wth O V I I n and S 2 1 R L M m M m s m 1 CM: IM 2 m 1 1 V 1 Borderlne: O VS I n wth V nv 2 1 R 2 L 1 M m s O S m CM f R > R 2n crt 2 L m 2 (1 )

More information

COLLEGE OF ENGINEERING PUTRAJAYA CAMPUS FINAL EXAMINATION SPECIAL SEMESTER 2013 / 2014

COLLEGE OF ENGINEERING PUTRAJAYA CAMPUS FINAL EXAMINATION SPECIAL SEMESTER 2013 / 2014 OLLEGE OF ENGNEENG PUTAJAYA AMPUS FNAL EXAMNATON SPEAL SEMESTE 03 / 04 POGAMME SUBJET ODE SUBJET : Bachelor of Electrcal & Electroncs Engneerng (Honours) Bachelor of Electrcal Power Engneerng (Honours)

More information

Department of Electrical and Computer Engineering FEEDBACK AMPLIFIERS

Department of Electrical and Computer Engineering FEEDBACK AMPLIFIERS Department o Electrcal and Computer Engneerng UNIT I EII FEEDBCK MPLIFIES porton the output sgnal s ed back to the nput o the ampler s called Feedback mpler. Feedback Concept: block dagram o an ampler

More information

A Simple Inventory System

A Simple Inventory System A Smple Inventory System Lawrence M. Leems and Stephen K. Park, Dscrete-Event Smulaton: A Frst Course, Prentce Hall, 2006 Hu Chen Computer Scence Vrgna State Unversty Petersburg, Vrgna February 8, 2017

More information

Lecture 20: Hypothesis testing

Lecture 20: Hypothesis testing Lecture : Hpothess testng Much of statstcs nvolves hpothess testng compare a new nterestng hpothess, H (the Alternatve hpothess to the borng, old, well-known case, H (the Null Hpothess or, decde whether

More information

Copyright 2004 by Oxford University Press, Inc.

Copyright 2004 by Oxford University Press, Inc. JT as an Amplfer &a Swtch, Large Sgnal Operaton, Graphcal Analyss, JT at D, asng JT, Small Sgnal Operaton Model, Hybrd P-Model, TModel. Lecture # 7 1 Drecton of urrent Flow & Operaton for Amplfer Applcaton

More information