A Reconfigurable System on Chip Implementation for Elliptic Curve Cryptography over GF(2 n )
|
|
- Shannon Cameron Wiggins
- 6 years ago
- Views:
Transcription
1 Reconfgurable System on Chp Implementaton for Ellptc Curve Cryptography over GF( n ) Mchael Jung, M. Ernst, F. Madlener, S. Huss, R. lümel Integrated Crcuts and Systems Lab Computer Scence Department Darmstadt Unversty of Technology Germany cv cryptovson GmbH Gelsenkrchen Germany
2 ECC over GF( n ) GF( n ) operatons mplemented n HW Square, Multply, dd Inverson wth Fermat s lttle Theorem Polynomal ase Multpler based on a novel, generalzed verson of the Karatsuba Ofman lgorthm EC level algorthms mplemented n SW P lgorthm for EC pont multplcaton Projectve Pont Coordnates lgorthmc flexblty combned wth performance [] Karatsuba and Ofman, Multplcaton of multdgt numbers on automata, 96 [] Lopez/Dahab, Fast multplcaton on ellptc curves over GF(m) wthout precomputatons, 999
3 8-t RISC Mcrocontroller + MHz tmel FPSLIC Reconfgurable System on Chp
4 8-t RISC Mcrocontroller FPG k Gate Equvalents Dstrbuted FreeRM TM Cells tmel FPSLIC Reconfgurable System on Chp
5 8-t RISC Mcrocontroller FPG 6 kyte RM Dual Ported Smultaneously accessble by MCU and FPG tmel FPSLIC Reconfgurable System on Chp
6 8-t RISC Mcrocontroller FPG 6 kyte RM Perpherals URTs Tmers etc. tmel FPSLIC Reconfgurable System on Chp
7 8-t RISC Mcrocontroller FPG 6 kyte RM Perpherals tmel FPSLIC Reconfgurable System on Chp Low cost, low complexty devce
8 Polynomal Karatsuba Multplcaton Polynomal Karatsuba Multplcaton n x a n b x ( ) ( ) ( ) ˆ ˆ x x ( ) T ( ) ( ) T ( ) T ˆ ) ( ˆ T x T T T T x * ( ) ( ) ˆ ˆ x x ˆ n x x wth
9 Mult Mult-Segment Segment Karatsuba Karatsuba (MSK) (MSK) + k k k k k x S x S MSK,, ˆ ), ( ˆ ), ( ), ( ), ( ), ( ), ( ), (,,,, M S S S l m m l m l m l m + ), ( ), (,, M S l l + + m l l m l l l m M, ), ( wth, and Generalzaton of the Karatsuba Multplcaton lgorthm Polynomals are splt nto an arbtrary number of segments
10 MSK k for k MSK wth M M M M M M,,,,,, ( M, ) ( M, M, M,) ( M, M, M,) ( M, M, M,) ( M ), xˆ xˆ xˆ xˆ xˆ ( ) ( ) ( ) ( ) ( ) ( ) *
11 Reorderng of Partal Products ) Pattern Groupng ) Reorder pattern by decreasng x n ) Mnmze dfferences n the set of ndces of adjacent pattern *
12 Pattern Groupng.) Groupng the partal products to one of three possble pattern *
13 Pattern Groupng.) Groupng the partal products to one of three possble pattern *
14 Pattern Groupng.) Groupng the partal products to one of three possble pattern *
15 Pattern Groupng.) Groupng the partal products to one of three possble pattern *
16 Pattern Groupng.) Groupng the partal products to one of three possble pattern *
17 Pattern Groupng.) Groupng the partal products to one of three possble pattern *
18 Reorderng of Partal Products ) Pattern Groupng ) Reorder pattern by decreasng x n ) Mnmze dfferences n the set of ndces of adjacent pattern *
19 Reorderng of Partal Products.) Orderng of the pattern n a top-left to bottom-rght fashon *
20 Reorderng of Partal Products.) Orderng of the pattern n a top-left to bottom-rght fashon *
21 Reorderng of Partal Products ) Pattern Groupng ) Reorder pattern by decreasng x n ) Mnmze dfferences n the set of ndces of adjacent pattern *
22 Reorderng of Partal Products.) Ensurng that the set of added up segments n the partal products dffers only by one element between two adjacent patterns *
23 Reorderng of Partal Products.) Ensurng that the set of added up segments n the partal products dffers only by one element between two adjacent patterns *
24 Multplcaton Sequence Mult
25 Multplcaton Sequence Mult Clock Cycle:
26 Multplcaton Sequence Mult Clock Cycle:
27 Multplcaton Sequence Mult Clock Cycle:
28 Multplcaton Sequence Mult Clock Cycle:
29 Multplcaton Sequence Mult Clock Cycle:
30 Multplcaton Sequence Mult Clock Cycle: 6
31 Multplcaton Sequence Mult Clock Cycle: 7
32 Multplcaton Sequence Mult Clock Cycle: 8
33 Coprocessor Interface VR EC level lgorthms FPG Fnte Feld rthmetc MULT DD RM Op. Op. SQURE FF-LU Controller...
34 Prototype Implementaton: -Segment Karatsuba (MSK ) -t combnatonal Multpler GF( ) Results FF-Level Operaton FF-Mult Clock Cycles best case worst case Operaton EC-Double Clock Cycles 9 FF-dd 6 6 EC-dd 6 FF-Square 9 k P, One EC pont multplcaton takes.9 MHz Speed-up factor of about compared to an assembler optmzed software mplementaton
Bit-Parallel Word-Serial Multiplier in GF(2 233 ) and Its VLSI Implementation. Dr. M. Ahmadi
Bt-Parallel Word-Seral Multpler n GF(2 233 ) and Its VLSI Implementaton Supervsors: Student: Dr. Huapeng Wu Dr. M. Ahmad Wenka Tang Contents Introducton to Fnte Feld Research Motvatons Proposed Multplers
More informationTOPICS 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 informationAn efficient algorithm for multivariate Maclaurin Newton transformation
Annales UMCS Informatca AI VIII, 2 2008) 5 14 DOI: 10.2478/v10065-008-0020-6 An effcent algorthm for multvarate Maclaurn Newton transformaton Joanna Kapusta Insttute of Mathematcs and Computer Scence,
More informationSpeeding 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 informationLectures - 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 informationVariability-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 informationApplication of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations
Applcaton of Nonbnary LDPC Codes for Communcaton over Fadng Channels Usng Hgher Order Modulatons Rong-Hu Peng and Rong-Rong Chen Department of Electrcal and Computer Engneerng Unversty of Utah Ths work
More informationThe Improved Montgomery Scalar Multiplication Algorithm with DPA Resistance Yanqi Xu, Lin Chen, Moran Li
nd Internatonal Conference on Electrcal, Computer Engneerng and Electroncs (ICECEE 015) The Improved Montgomery Scalar Multplcaton Algorthm wth DPA Resstance Yanq Xu, Ln Chen, Moran L Informaton Scence
More informationFrom Biot-Savart Law to Divergence of B (1)
From Bot-Savart Law to Dvergence of B (1) Let s prove that Bot-Savart gves us B (r ) = 0 for an arbtrary current densty. Frst take the dvergence of both sdes of Bot-Savart. The dervatve s wth respect to
More informationRISC Processors. Hierarchical VLSI Design. Multiple Layered Architecture. 6. Case Study: Formal Verification of RISC Processors using HOL
6. Case Study: Formal Verfcaton of RISC Processors usng HOL RISC Processors Motvaton RISC Verfcaton Model Dervng Formal Specfcatons Verfcaton Tasks Ppelne Correctness Processor Specfc Defntons Expermental
More informationCS 770G - Parallel Algorithms in Scientific Computing
References CS 770G - Parallel Algorthms n Scentfc Computng Parallel Sortng Introducton to Parallel Computng Kumar, Grama, Gupta, Karyps, Benjamn Cummngs. A porton of the notes comes from Prof. J. Demmel
More informationSome 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 informationCurve Fitting with the Least Square Method
WIKI Document Number 5 Interpolaton wth Least Squares Curve Fttng wth the Least Square Method Mattheu Bultelle Department of Bo-Engneerng Imperal College, London Context We wsh to model the postve feedback
More informationInner Product. Euclidean Space. Orthonormal Basis. Orthogonal
Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,
More informationScalable RSA Processor in Reconfigurable Hardware - a SoC Building Block
Scalable RSA Processor n Reconfgurable Hardare - a SoC Buldng Block Vktor Fscher and Mloš Drutarovský Laboratore ratement du Sgnal et Instrumentaton, Unté Mxte de Recherche CNRS 556, Unversté Jean Monnet,
More informationDepartment 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 informationEffective 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 informationCS 331 DESIGN AND ANALYSIS OF ALGORITHMS DYNAMIC PROGRAMMING. Dr. Daisy Tang
CS DESIGN ND NLYSIS OF LGORITHMS DYNMIC PROGRMMING Dr. Dasy Tang Dynamc Programmng Idea: Problems can be dvded nto stages Soluton s a sequence o decsons and the decson at the current stage s based on the
More informationU.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017
U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that
More informationThe 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 informationChapter 6. BCH Codes
Wreless Informaton Transmsson System Lab Chapter 6 BCH Codes Insttute of Communcatons Engneerng Natonal Sun Yat-sen Unversty Outlne Bnary Prmtve BCH Codes Decodng of the BCH Codes Implementaton of Galos
More informationLecture 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 informationGeneralized 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 informationfind (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 informationContinued..& Multiplier
CS222: Computer Arthmetc : Adder Contnued..& Multpler Dr. A. Sahu Dept of Comp. Sc. & Engg. Indan Insttute of Technology Guwahat 1 Outlne Adder Unversal Use (N bt addton) RppleCarry Adder, Full Adder,
More informationQueueing 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 informationTemperature. Chapter Heat Engine
Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the
More informationExhaustive Search for the Binary Sequences of Length 2047 and 4095 with Ideal Autocorrelation
Exhaustve Search for the Bnary Sequences of Length 047 and 4095 wth Ideal Autocorrelaton 003. 5. 4. Seok-Yong Jn and Hong-Yeop Song. Yonse Unversty Contents Introducton Background theory Ideal autocorrelaton
More informationProfessor Terje Haukaas University of British Columbia, Vancouver The Q4 Element
Professor Terje Haukaas Unversty of Brtsh Columba, ancouver www.nrsk.ubc.ca The Q Element Ths document consders fnte elements that carry load only n ther plane. These elements are sometmes referred to
More informationPerformance Analysis of the Postcomputation- Based Generic-Point Parallel Scalar Multiplication Method
P a g e 3 Vol. 1 Issue 11 (Ver. 1.) October 1 Global Journal of Computer Scence and Technology Performance Analyss of the Postcomputaton- Based Generc-Pont Parallel Scalar Multplcaton Method Tur F. Al-Soman
More informationVector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence.
Vector Norms Chapter 7 Iteratve Technques n Matrx Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematcs Unversty of Calforna, Berkeley Math 128B Numercal Analyss Defnton A vector norm
More informationEfficient FPGA-based Karatsuba multipliers for polynomials over F 2
JOACHIM VON ZUR GATHEN & JAMSHID SHOKROLLAHI (2005). Efficient FPGA-based Karatsuba multipliers for polynomials over F2. In Selected Areas in Cryptography (SAC 2005), BART PRENEEL & STAFFORD TAVARES, editors,
More informationELECTRONICS. 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 informationOutline 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 informationExercises. 18 Algorithms
18 Algorthms Exercses 0.1. In each of the followng stuatons, ndcate whether f = O(g), or f = Ω(g), or both (n whch case f = Θ(g)). f(n) g(n) (a) n 100 n 200 (b) n 1/2 n 2/3 (c) 100n + log n n + (log n)
More informationMMA and GCMMA two methods for nonlinear optimization
MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons
More informationEEE 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 informationComputing Correlated Equilibria in Multi-Player Games
Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,
More informationFlexible Quantization
wb 06/02/21 1 Flexble Quantzaton Bastaan Klejn KTH School of Electrcal Engneerng Stocholm wb 06/02/21 2 Overvew Motvaton for codng technologes Basc quantzaton and codng Hgh-rate quantzaton theory wb 06/02/21
More informationFast Tree-Structured Recursive Neural Tensor Networks
Fast Tree-Structured ecursve Neural Tensor Networks Anand Avat, Na-Cha Chen Stanford Unversty avat@csstanfordedu, ncchen@stanfordedu Project TA: Youssef Ahres 1 Introducton In ths project we explore dfferent
More informationUsing the Minimum Set of Input Combinations to Minimize the Area of Local Routing Networks in Logic Clusters. FPGAs. Andy Ye Ryerson University
Usng the Mnmum Set of Input Combnatons to Mnmze the Area of Local Routng Networks n Logc Clusters Contanng Logcally Equvalent I/Os n FPGAs Andy Ye Ryerson Unversty Mnmum-area IIBs how to mnmze the area
More informationEfficient Fixed Base Exponentiation and Scalar Multiplication based on a Multiplicative Splitting Exponent Recoding
Effcent Fxed Base Exponentaton and Scalar Multplcaton based on a Multplcatve Splttng Exponent Recodng Jean-Marc Robert, Chrstophe Negre, Thomas Plantard To cte ths verson: Jean-Marc Robert, Chrstophe Negre,
More informationCase A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k.
THE CELLULAR METHOD In ths lecture, we ntroduce the cellular method as an approach to ncdence geometry theorems lke the Szemeréd-Trotter theorem. The method was ntroduced n the paper Combnatoral complexty
More informationA 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 informationTuring 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 informationHigh-Speed Low-Complexity Reed-Solomon Decoder using Pipelined Berlekamp-Massey Algorithm and Its Folded Architecture
JOURNAL OF SEMICONUCTOR TECHNOLOGY AN SCIENCE, VOL., NO.3, SEPTEMBER, 2 93 Hgh-Speed Low-Complexty Reed-Solomon ecoder usng Ppelned Berlekamp-Massey Algorthm and Its Folded Archtecture Jeong-In Park, Khoon
More informationOn a Parallel Implementation of the One-Sided Block Jacobi SVD Algorithm
Jacob SVD Gabrel Okša formulaton One-Sded Block-Jacob Algorthm Acceleratng Parallelzaton Conclusons On a Parallel Implementaton of the One-Sded Block Jacob SVD Algorthm Gabrel Okša 1, Martn Bečka, 1 Marán
More informationPower Efficient Design and Implementation of a Novel Constant Correction Truncated Multiplier
APSIPA ASC 11 X an Power Effcent Desgn and Implementaton of a Novel Constant Correcton Truncated Multpler Yu Ren, Dong Wang, Lebo Lu, Shouy Yn and Shaojun We Tsnghua Unversty, Bejng E-mal: reneereny@gmal.com
More informationLeast squares cubic splines without B-splines S.K. Lucas
Least squares cubc splnes wthout B-splnes S.K. Lucas School of Mathematcs and Statstcs, Unversty of South Australa, Mawson Lakes SA 595 e-mal: stephen.lucas@unsa.edu.au Submtted to the Gazette of the Australan
More informationEntanglement vs Discord: Who Wins?
Entanglement vs Dscord: Who Wns? Vlad Gheorghu Department of Physcs Carnege Mellon Unversty Pttsburgh, PA 15213, U.S.A. Januray 20, 2011 Vlad Gheorghu (CMU) Entanglement vs Dscord: Who Wns? Januray 20,
More informationSome Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)
Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998
More informationCOMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering,
COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION Erdem Bala, Dept. of Electrcal and Computer Engneerng, Unversty of Delaware, 40 Evans Hall, Newar, DE, 976 A. Ens Cetn,
More informationReport on Image warping
Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.
More informationAnalytical Gradient Evaluation of Cost Functions in. General Field Solvers: A Novel Approach for. Optimization of Microwave Structures
IMS 2 Workshop Analytcal Gradent Evaluaton of Cost Functons n General Feld Solvers: A Novel Approach for Optmzaton of Mcrowave Structures P. Harscher, S. Amar* and R. Vahldeck and J. Bornemann* Swss Federal
More informationDifferential Polynomials
JASS 07 - Polynomals: Ther Power and How to Use Them Dfferental Polynomals Stephan Rtscher March 18, 2007 Abstract Ths artcle gves an bref ntroducton nto dfferental polynomals, deals and manfolds and ther
More informationOne-Way Quantum Computer Simulation
One-Way Quantum Computer Smulaton Eesa Nkahd, Mahboobeh Houshmand, Morteza Saheb Zaman, Mehd Sedgh Quantum Desgn Automaton Lab Department of Computer Engneerng and Informaton Technology Amrkabr Unversty
More informationp 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 informationAn Efficient Eligible Error Locator Polynomial Searching Algorithm and Hardware Architecture for One-Pass Chase BCH Codes Decoding
Ths artcle has been accepted for publcaton n a future ssue of ths journal, but has not been fully edted. Content may change pror to fnal publcaton. Ctaton nformaton: DOI.9/TCSII.6.58587, IEEE An Effcent
More informationCalculation 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 informationDecision Diagrams Derivatives
Decson Dagrams Dervatves Logc Crcuts Desgn Semnars WS2010/2011, Lecture 3 Ing. Petr Fšer, Ph.D. Department of Dgtal Desgn Faculty of Informaton Technology Czech Techncal Unversty n Prague Evropský socální
More informationHighly Efficient Gradient Computation for Density-Constrained Analytical Placement Methods
Hghly Effcent Gradent Computaton for Densty-Constraned Analytcal Placement Methods Jason Cong and Guoje Luo UCLA Computer Scence Department { cong, gluo } @ cs.ucla.edu Ths wor s partally supported by
More informationMACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression
11 MACHINE APPLIED MACHINE LEARNING LEARNING MACHINE LEARNING Gaussan Mture Regresson 22 MACHINE APPLIED MACHINE LEARNING LEARNING Bref summary of last week s lecture 33 MACHINE APPLIED MACHINE LEARNING
More informationDynamic 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 informationMeasurement and Model Identification of Semiconductor Devices
Measurement and Model Identfcaton of Semconductor evces JOSEF OBEŠ MARTIN GRÁBNER epartment of Rado Engneerng Faculty of Electrcal Engneerng zech Techncal Unversty n Prague Techncá 2 6627 Praha 6 zech
More informationSalmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2
Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to
More informationIntroduction to Algorithms
Introducton to Algorthms 6.046J/8.40J Lecture 7 Prof. Potr Indyk Data Structures Role of data structures: Encapsulate data Support certan operatons (e.g., INSERT, DELETE, SEARCH) Our focus: effcency of
More informationADVANCED MACHINE LEARNING ADVANCED MACHINE LEARNING
1 ADVANCED ACHINE LEARNING ADVANCED ACHINE LEARNING Non-lnear regresson technques 2 ADVANCED ACHINE LEARNING Regresson: Prncple N ap N-dm. nput x to a contnuous output y. Learn a functon of the type: N
More informationEnsemble Methods: Boosting
Ensemble Methods: Boostng Ncholas Ruozz Unversty of Texas at Dallas Based on the sldes of Vbhav Gogate and Rob Schapre Last Tme Varance reducton va baggng Generate new tranng data sets by samplng wth replacement
More informationVQ 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 informationSolution of Equilibrium Equation in Dynamic Analysis. Mode Superposition. Dominik Hauswirth Method of Finite Elements II Page 1
Soluton of Equlbrum Equaton n Dynamc Analyss Mode Superposton Domnk Hauswrth..7 Method of Fnte Elements II Page Contents. Mode Superposton: Idea and Equatons. Example 9.7 3. Modes 4. Include Dampng 5.
More informationWe present the algorithm first, then derive it later. Assume access to a dataset {(x i, y i )} n i=1, where x i R d and y i { 1, 1}.
CS 189 Introducton to Machne Learnng Sprng 2018 Note 26 1 Boostng We have seen that n the case of random forests, combnng many mperfect models can produce a snglodel that works very well. Ths s the dea
More information10) Activity analysis
3C3 Mathematcal Methods for Economsts (6 cr) 1) Actvty analyss Abolfazl Keshvar Ph.D. Aalto Unversty School of Busness Sldes orgnally by: Tmo Kuosmanen Updated by: Abolfazl Keshvar 1 Outlne Hstorcal development
More informationFormulas for the Determinant
page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use
More information2.29 Numerical Fluid Mechanics Fall 2011 Lecture 12
REVIEW Lecture 11: 2.29 Numercal Flud Mechancs Fall 2011 Lecture 12 End of (Lnear) Algebrac Systems Gradent Methods Krylov Subspace Methods Precondtonng of Ax=b FINITE DIFFERENCES Classfcaton of Partal
More informationSURVIVABLE DISTRIBUTED STORAGE WITH PROGRESSIVE DECODING
SURVIVABLE DISTRIBUTED STORAGE WITH PROGRESSIVE DECODING Yunghsang S. Han, Soj Omwade, and Rong Zheng Department of Computer Scence Unversty of Houston Houston, T, 7704, USA http://www.cs.uh.edu Techncal
More informationMoments of Inertia. and reminds us of the analogous equation for linear momentum p= mv, which is of the form. The kinetic energy of the body is.
Moments of Inerta Suppose a body s movng on a crcular path wth constant speed Let s consder two quanttes: the body s angular momentum L about the center of the crcle, and ts knetc energy T How are these
More informationPhysics 4B. A positive value is obtained, so the current is counterclockwise around the circuit.
Physcs 4B Solutons to Chapter 7 HW Chapter 7: Questons:, 8, 0 Problems:,,, 45, 48,,, 7, 9 Queston 7- (a) no (b) yes (c) all te Queston 7-8 0 μc Queston 7-0, c;, a;, d; 4, b Problem 7- (a) Let be the current
More information5012: VLSI Signal Processing
5: VLSI Sgnal Processng Lecture 6 Fast Algortms for Dgtal Sgnal Processng VSP Lecture6 - Fast Algortms for DSP (cwlu@twns.ee.nctu.edu.tw) - Algortm Strengt Reducton Motvaton Te number of strong operatons,
More informationON OPTIMIZING A CLASS OF MULTI-DIMENSIONAL LOOPS WITH REDUCTIONS FOR PARALLEL EXECUTION
ON OPTIMIZING A CLASS OF MULTI-DIMENSIONAL LOOPS WITH REDUCTIONS FOR PARALLEL EXECUTION CHI-CHUNG LAM*, P. SADAYAPPAN*, AND REPHAEL WENGER Department of Computer and Informaton Scence, The Oho State Unversty
More informationProblem Solving in Math (Math 43900) Fall 2013
Problem Solvng n Math (Math 43900) Fall 2013 Week four (September 17) solutons Instructor: Davd Galvn 1. Let a and b be two nteger for whch a b s dvsble by 3. Prove that a 3 b 3 s dvsble by 9. Soluton:
More informationLecture 3: Dual problems and Kernels
Lecture 3: Dual problems and Kernels C4B Machne Learnng Hlary 211 A. Zsserman Prmal and dual forms Lnear separablty revsted Feature mappng Kernels for SVMs Kernel trck requrements radal bass functons SVM
More informationNonlinear Classifiers II
Nonlnear Classfers II Nonlnear Classfers: Introducton Classfers Supervsed Classfers Lnear Classfers Perceptron Least Squares Methods Lnear Support Vector Machne Nonlnear Classfers Part I: Mult Layer Neural
More informationFor 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 informationPHYS 705: Classical Mechanics. Calculus of Variations II
1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary
More informationMEM 255 Introduction to Control Systems Review: Basics of Linear Algebra
MEM 255 Introducton to Control Systems Revew: Bascs of Lnear Algebra Harry G. Kwatny Department of Mechancal Engneerng & Mechancs Drexel Unversty Outlne Vectors Matrces MATLAB Advanced Topcs Vectors A
More informationInternational Journal of Mathematical Archive-3(3), 2012, Page: Available online through ISSN
Internatonal Journal of Mathematcal Archve-3(3), 2012, Page: 1136-1140 Avalable onlne through www.ma.nfo ISSN 2229 5046 ARITHMETIC OPERATIONS OF FOCAL ELEMENTS AND THEIR CORRESPONDING BASIC PROBABILITY
More informationFaster ECC over F 2. (feat. PMULL)
Faster ECC over F 2 571 (feat. PMULL) Hwajeong Seo 1 Institute for Infocomm Research (I2R), Singapore hwajeong84@gmail.com Abstract. In this paper, we show efficient elliptic curve cryptography implementations
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationLecture 4: September 12
36-755: Advanced Statstcal Theory Fall 016 Lecture 4: September 1 Lecturer: Alessandro Rnaldo Scrbe: Xao Hu Ta Note: LaTeX template courtesy of UC Berkeley EECS dept. Dsclamer: These notes have not been
More informationSUPER PRINCIPAL FIBER BUNDLE WITH SUPER ACTION
talan journal of pure appled mathematcs n. 33 2014 (63 70) 63 SUPER PRINCIPAL FIBER BUNDLE WITH SUPER ACTION M.R. Farhangdoost Department of Mathematcs College of Scences Shraz Unversty Shraz, 71457-44776
More information2.29 Numerical Fluid Mechanics
REVIEW Lecture 10: Sprng 2015 Lecture 11 Classfcaton of Partal Dfferental Equatons PDEs) and eamples wth fnte dfference dscretzatons Parabolc PDEs Ellptc PDEs Hyperbolc PDEs Error Types and Dscretzaton
More informationKernel 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 informationTowards strong security in embedded and pervasive systems: energy and area optimized serial polynomial multipliers in GF(2 k )
Towards strong securty n ebedded and pervasve systes: energy and area optzed seral polynoal ultplers n GF( k ) Zoya Dyka, Peter Langendoerfer, Frank Vater and Steffen Peter IHP, I Technologepark 5, D-53
More informationMDL-Based Unsupervised Attribute Ranking
MDL-Based Unsupervsed Attrbute Rankng Zdravko Markov Computer Scence Department Central Connectcut State Unversty New Brtan, CT 06050, USA http://www.cs.ccsu.edu/~markov/ markovz@ccsu.edu MDL-Based Unsupervsed
More informationPlans. Memoryless plans Definition. Memoryless plans Example. Images Formal definition. Images. Preimages. Preimages. Definition
Noetermnstc plannng (May 25, 2005) Plans AND-OR search Dynamc programmng (Albert-Ludwgs-Unverstät Freburg) 1 / 56 Plans Plans 1. map a state/an observaton to an operator. We use ths defnton of plans for
More informationFixed points of IA-endomorphisms of a free metabelian Lie algebra
Proc. Indan Acad. Sc. (Math. Sc.) Vol. 121, No. 4, November 2011, pp. 405 416. c Indan Academy of Scences Fxed ponts of IA-endomorphsms of a free metabelan Le algebra NAIME EKICI 1 and DEMET PARLAK SÖNMEZ
More informationFast arithmetic for polynomials over F 2 in hardware
Fast arithmetic for polynomials over F 2 in hardware JOACHIM VON ZUR GATHEN & JAMSHID SHOKROLLAHI (200). Fast arithmetic for polynomials over F2 in hardware. In IEEE Information Theory Workshop (200),
More informationIntroduction to Algorithms
Introducton to Algorthms 6.046J/18.401J Lecture 7 Prof. Potr Indyk Data Structures Role of data structures: Encapsulate data Support certan operatons (e.g., INSERT, DELETE, SEARCH) What data structures
More informationA NEW ALGORITHM FOR THE RECURSION OF HYPERGEOMETRIC MULTISUMS WITH IMPROVED UNIVERSAL DENOMINATOR
A NEW ALGORITHM FOR THE RECURSION OF HYPERGEOMETRIC MULTISUMS WITH IMPROVED UNIVERSAL DENOMINATOR STAVROS GAROUFALIDIS AND XINYU SUN Abstract. The purpose of the paper s to ntroduce two new algorthms.
More informationDeformation rate estimation on changing landscapes using. Abstract Title. Temporarily Coherent Point InSAR. Author name
Deformaton rate estmaton on changng landscapes usng Abstract Ttle Temporarly Coherent Pont InSAR Le Zhang (1), Xaol Dng (1) and Zhong Lu (2) Author name (1)The Hong Kong Polytechnc Unversty, Kowloon, Hong
More information