Datapath&Design&Approaches. Increasing&the&Initiation&Rate
|
|
- John Watkins
- 5 years ago
- Views:
Transcription
1 atapath&esign&approaches Previous&Topic,&estimate&minimum&number&of& resources&and&then&try&to&schedule&them May&equire&Iteration,&can&ive&minimum&resource& datapath Another&approach&is&to&build&a&schedule&first& (based&on&the&specifications),&then&determine& required&resources Can&meet&latency&specification&immediately,&but& may&exceed&area&limitations Increasing&the&Initiation&ate X a0 X@1 a1 X@2 a2 X@3 a3 Y N2 N3 N4 N5 N6 N7 N8 4"Clocks" equired"if" Non3 chained The&flowgraph&below&has&a&longest&path&of&4&clocks.&&This& means&the&computations&cannot&be&completed&in&less&than& 4&clocks&(min.&latency)&because&of&data&dependencies. However,(we(CAN( increase(the( initiation(rate((rate(at( which(new(input( values(are( accepted)
2 Latency&=&4 Make&No&Assumptions&About&esources&Yet Lets&look&at&the&operations&needed&with&latency=4&for& several&clock&cycles.&& Successive&Sample&values&are&labeled&A,B,C&etc. Sample A Sample B Sample C 1 N4() N5(), Input x 2 N2(),N3(),N7() 3 N6 () 4 N8() Initiation&Period&=&2 Make&No&Assumptions&About&esources&Yet Lets&look&at&the&operations&needed&with&initiation&period&=& 2&and&latency=4&for&several&clock&cycles.&& Successive&Sample&values&are&labeled&A,B,C&etc. Sample A Sample B Sample C 1 N4() N5(), Input x 2 N2(),N3(),N7() 3 N6 () N4() N5(), Input x 4 N8() N2(),N3(),N7() 5 N6() 6 N8() 7 8
3 Initiation&Period&=&2 Lets&look&at&the&operations&needed&with&initiation&period&=& 2&for&several&clock&cycles.&&Successive&Sample&values& are&labeled&a,b,c&etc. Sample A Sample B Sample C 1 N4() N5(), Input x 2 N2(),N3(),N7() 3 N6 () N4() N5(), Input x 4 N8() N2(),N3(),N7() 5 N6() N4() N5(), Input x 6 N8() N2(),N3(),N7() 7 N6() 8 N8() esources Sample A Sample B Sample C 1 N4() N5(), Input x 2 N2(),N3(),N7() 3 N6() N4() N5(), Input x 4 N8() N2(),N3(),N7() 5 N6() N4() N5(), Input x 6 N8() N2(),N3(),N7() 7 N6() 8 N8() Two&multiplies&per&clock,&so&need&two&multipliers&(A,&B). In&Clock,&Clock&we&have&two&additions,&so&need&two& adders&(a,&b).
4 Initiation&Period&and&Latency The&initiation&period of&this&design&is&2. The latency is&4.& When&initiation&period latency,&then&system&level& pipelining is&occurring&because&the&computations&for& more&than&one&input&sample&are&being&accomplished& in&a&single&clock&period. Pipelining&implies¶llelism&(in&time)&T more&than& one&sample&computation&is&in&progress&at&any&given& clock&cycle. To&schedule,&need&to&generalize&the&table. eneralized&schedule Sample J-1: Sample J Sample J1 I-2 N4() N5(), Input X I-1 N2(),N3(),N7() I0 N6() N4() N5(), Input X I1 N8() N2(),N3(),N7() I2 N6() N4() N5(), Input X I3 N8() N2(),N3(),N7()
5 Schedule&&I0 What&do&we&need&in&egisters&at&Clock&I0? Schedule&&IT1:& or&sample J-1: N2, N3, N7 or&sample J: or&sample J1: No&operations egisters: A: B: C: : N2, : N3, :N7 Schedule&&I0: Sample&J-1: N6(N3N7) overwrite&n7 value,&no&longer&needed Sample&J: Input&x x overwrite N3 value, no&longer&needed Sample&J: N4(x@2a2) Ba2 add&new®ister to&hold N4 Sample&J: N5(x@3a3) A Aa1 overwrite&x@3 value,&no&longer&needed inished:((added(extra(egister Schedule&&I1 egisters: A: N5, B: x@2, C: x@1, : N2, : x, :N6, : N4 After&Clock,&egisters&need&to&be&setup&for&next&clock: A: x@3, B: x@2, C: x@1, : N2, : N3, :N7 Schedule&&I1: Sample&J-1: N8(N2N6) Y output&goes&to Y bus Sample&J: N2(xa0) a0 overwrite&old N2 value,&no&longer needed Important&that N2 go&into because&this&is&needed&for&next&clock&cycle. Sample&J: N3(x@1a1) Ca1 Need&=N3 for&next&clock x value&is¤tly&in.&&in&the&next&clock, x= x@1 for&sample J1, so&move x into&c register. Sample&J1: C=x@1 C J1:x@1 = J:x Sample J: N7(N4N5) A Need&N7 in& for&j1 sample Sample&J1: A=x@3 A B J1: x@3 = J:x@2 Sample&J1: B=x@2 B C J1: x@2 = J:x@1 inished:((no(extra(registers(needed
6 Schedule:&s&&I2,&I3 Schedule&for&&I2&is&repeat&of&&I0!!! Schedule&for&&I3&is&repeat&of&&I1!! Actually,&generalized&schedule&only&needs& two&clocks esource&comparison Init ate esources Multipliers Adders egisters oubling&the&initiation&rate&did¬&double&the& hardware&resources&needed.&&&why? Because&xecution&units&for&Initiation&period&=&4& were¬&fully&utilized!
7 xecution&unit&utilization Init ate xecution Unit Utilization Mult A Mult B Add A Add B 4 50% (2/4) 50 (2/4) 75% (3/4) N/A 2 100% (2/2) 100% (2/2) 100% (2/2) 50% (1/2) Note&that&multipliers&were¬&fully&utilized&for&Initiation& period&=&2, we&used&this&free&time&in&the&schedule&for&initiation& period&=&4. Init&Period&=&1 Sample A Sample B Sample C Sample 1 N4() N5(), Input x 2 N2(),N3(), N7() N4() N5(), Input x 3 N6() N2(),N3(),N7() N4() N5(), Input x 4 N8() N6() N2(),N3() N4() N5(), Input x,n7() 5 N8() N6() N2(),N3(), N7() 6 N8() N6() 7 N8() our&multiplies&in&clock&4,&so&need&four&multipliers: (A,&B,&C,&). Three&additions&in&clock&4,&so&need&three&adders:& (A,&B,&C).
8 esource&comparison&again Latency&for&all&of&these&designs&is&4&clocks. This&table&clearly&illustrates&the&time&versus&area& tradeoff&in&igital&systems. Will&cost&you&MO&resources&to&do&something&in& LSS&Time! Less&Clock&cycles,&Lower&Clock&Period Computation&Time&=&#of&Clocks Clock&Period& Throughput&=&1/(Computation&Time) Increasing&the&initiation&period&will&increase&the& computation&rate&in&terms&of&clock&cycles Less&clock&cycles&between&new&outputs To&decrease&the&clock&period&(increase&clock& frequency),&need&to&have&shorter&combinational& paths&in&the&design PIPLIN&the&individual&execution&units Multiplier&will&have&much&longer&delay&than&adder,&so&will& want&to&pipeline&this&first
9 ecall&pipelining MULTIPLI MULTIPLI two(stages MULTIPLI three(stages MULTIPLI four(stages one(stage Assume&Multiplier&with&2TStage&Pipeline Schedule&with&Initiation&Period&=&Latencyb&2&mult,&1&adder Scheduling&now&takes&6&clocks.&&&N7 depends&on&n5, N4 T can t&do&this&until&&&4&because&multiplier&result¬&ready.
10 lowgraph&annotated&with&elay& Values&(weighted&A) x a0 N2 3 clks 3 clks N3 a1 x@2 3 clks N8 Y N4 N6 a2 3 clks x@3 N7 N5 a3 Longest(path( is(now(6( clocks ecreasing&initiation&period&to&2
11 ecreasing&initiation&period&to&2 Schedule&with&Initiation&Period&=2 Schedule&with&Initiation&Period&=&Latencyb&2&mult,&2&adders Adder A Adder B MultA MultB IO I N6 (j-2) idle N5 (j) N4 (j) Input x I1 N8 (j-2) N7 (j-1) N3 (j) N2(j) I2 N6 (j-1) idle N5(j1) N4(j1) Input x I3 N8 (j-1) N7 (j) N3(j1) N3(j1) I4 N6 (j) idle N5(j2) N4(j2) Input x I5 N8 (j) N7 (j1) N3(j2) N3(j2) eneral&schedule&is&i,&i1.&&6&clocks&shown&to&complete&one&&computation.&& Initiation&Period&=&2,&&Latency&=&6.&&&&Overlapping&computations&of&three& samples.
Pipelining and Parallel Processing
Pipelining and Parallel Processing ( 范倫達 ), Ph. D. Department of Computer Science National Chiao Tung University Taiwan, R.O.C. Fall, 010 ldvan@cs.nctu.edu.tw http://www.cs.nctu.edu.tw/~ldvan/ Outlines
More informationProfessor Fearing EECS150/Problem Set Solution Fall 2013 Due at 10 am, Thu. Oct. 3 (homework box under stairs)
Professor Fearing EECS50/Problem Set Solution Fall 203 Due at 0 am, Thu. Oct. 3 (homework box under stairs). (25 pts) List Processor Timing. The list processor as discussed in lecture is described in RT
More informationPipelining and Parallel Processing
Pipelining and Parallel Processing Lan-Da Van ( 倫 ), Ph. D. Department of omputer Science National hiao Tung University Taiwan, R.O.. Spring, 007 ldvan@cs.nctu.edu.tw http://www.cs.nctu.edu.tw/~ldvan/
More informationBinary Multipliers. Reading: Study Chapter 3. The key trick of multiplication is memorizing a digit-to-digit table Everything else was just adding
Binary Multipliers The key trick of multiplication is memorizing a digit-to-digit table Everything else was just adding 2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9 2 2 4 6 8 2 4 6 8 3 3 6 9 2 5 8 2 24 27 4 4 8 2 6
More informationDigital Integrated Circuits A Design Perspective. Arithmetic Circuits. Jan M. Rabaey Anantha Chandrakasan Borivoje Nikolic.
Digital Integrated Circuits A Design Perspective Jan M. Rabaey Anantha Chandrakasan Borivoje Nikolic Arithmetic Circuits January, 2003 1 A Generic Digital Processor MEM ORY INPUT-OUTPUT CONTROL DATAPATH
More informationICS 6N Computational Linear Algebra Matrix Algebra
ICS 6N Computational Linear Algebra Matrix Algebra Xiaohui Xie University of California, Irvine xhx@uci.edu February 2, 2017 Xiaohui Xie (UCI) ICS 6N February 2, 2017 1 / 24 Matrix Consider an m n matrix
More informationPipelining and Parallel Processing
Pipelining and Parallel Processing Pipelining ---reduction in the critical path increase the clock speed, or reduce power consumption at same speed Parallel Processing ---multiple outputs are computed
More informationMatrix operations Linear Algebra with Computer Science Application
Linear Algebra with Computer Science Application February 14, 2018 1 Matrix operations 11 Matrix operations If A is an m n matrix that is, a matrix with m rows and n columns then the scalar entry in the
More informationVLSI Signal Processing
VLSI Signal Processing Lecture 1 Pipelining & Retiming ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-1 Introduction DSP System Real time requirement Data driven synchronized by data
More informationReview for Final Exam
CSE140: Components and Design Techniques for Digital Systems Review for Final Exam Mohsen Imani CAPE Please submit your evaluations!!!! RTL design Use the RTL design process to design a system that has
More informationEECS150 - Digital Design Lecture 11 - Shifters & Counters. Register Summary
EECS50 - Digital Design Lecture - Shifters & Counters February 24, 2003 John Wawrzynek Spring 2005 EECS50 - Lec-counters Page Register Summary All registers (this semester) based on Flip-flops: q 3 q 2
More informationDigital Integrated Circuits A Design Perspective. Arithmetic Circuits. Jan M. Rabaey Anantha Chandrakasan Borivoje Nikolic.
Digital Integrated Circuits A Design Perspective Jan M. Rabaey Anantha Chandrakasan Borivoje Nikolic Arithmetic Circuits January, 2003 1 A Generic Digital Processor MEMORY INPUT-OUTPUT CONTROL DATAPATH
More informationMath 4377/6308 Advanced Linear Algebra
2.3 Composition Math 4377/6308 Advanced Linear Algebra 2.3 Composition of Linear Transformations Jiwen He Department of Mathematics, University of Houston jiwenhe@math.uh.edu math.uh.edu/ jiwenhe/math4377
More informationDivide and Conquer algorithms
Divide and Conquer algorithms Another general method for constructing algorithms is given by the Divide and Conquer strategy. We assume that we have a problem with input that can be split into parts in
More informationDatapath Component Tradeoffs
Datapath Component Tradeoffs Faster Adders Previously we studied the ripple-carry adder. This design isn t feasible for larger adders due to the ripple delay. ʽ There are several methods that we could
More informationMath 24 Spring 2012 Questions (mostly) from the Textbook
Math 24 Spring 2012 Questions (mostly) from the Textbook 1. TRUE OR FALSE? (a) The zero vector space has no basis. (F) (b) Every vector space that is generated by a finite set has a basis. (c) Every vector
More informationCost/Performance Tradeoffs:
Cost/Performance Tradeoffs: a case study Digital Systems Architecture I. L10 - Multipliers 1 Binary Multiplication x a b n bits n bits EASY PROBLEM: design combinational circuit to multiply tiny (1-, 2-,
More informationL15: Custom and ASIC VLSI Integration
L15: Custom and ASIC VLSI Integration Average Cost of one transistor 10 1 0.1 0.01 0.001 0.0001 0.00001 $ 0.000001 Gordon Moore, Keynote Presentation at ISSCC 2003 0.0000001 '68 '70 '72 '74 '76 '78 '80
More informationOQ4867. Let ABC be a triangle and AA 1 BB 1 CC 1 = {M} where A 1 BC, B 1 CA, C 1 AB. Determine all points M for which ana 1...
764 Octogon Mathematical Magazine, Vol. 24, No.2, October 206 Open questions OQ4867. Let ABC be a triangle and AA BB CC = {M} where A BC, B CA, C AB. Determine all points M for which 4 s 2 3r 2 2Rr AA
More informationHardware implementations of ECC
Hardware implementations of ECC The University of Electro- Communications Introduction Public- key Cryptography (PKC) The most famous PKC is RSA and ECC Used for key agreement (Diffie- Hellman), digital
More informationSection A.6. Solving Equations. Math Precalculus I. Solving Equations Section A.6
Section A.6 Solving Equations Math 1051 - Precalculus I A.6 Solving Equations Simplify 1 2x 6 x + 2 x 2 9 A.6 Solving Equations Simplify 1 2x 6 x + 2 x 2 9 Factor: 2x 6 = 2(x 3) x 2 9 = (x 3)(x + 3) LCD
More informationx 9 or x > 10 Name: Class: Date: 1 How many natural numbers are between 1.5 and 4.5 on the number line?
1 How many natural numbers are between 1.5 and 4.5 on the number line? 2 How many composite numbers are between 7 and 13 on the number line? 3 How many prime numbers are between 7 and 20 on the number
More informationSolutions to Chapter Review Questions, Chapter 0
Instructor s Solutions Manual, Chapter 0 Review Question 1 Solutions to Chapter Review Questions, Chapter 0 1. Explain how the points on the real line correspond to the set of real numbers. solution Start
More informationMatrix Representation
Matrix Representation Matrix Rep. Same basics as introduced already. Convenient method of working with vectors. Superposition Complete set of vectors can be used to express any other vector. Complete set
More information8 x 8 x 8 x 8 x x x 1 x 1 x 1 x x 7. 7 x (4 3-6) x x ( ) 20 5 x 2 - (6 + 2) x 7
Worksheet # Order of Operations & Exponents Write in exponent form. Find the value. x x x x x 0 x x x x 0 Calculate. + - x 0 x ( - ) x - x ( + ) 0 x - ( + ) x Worksheet # Least Common Multiple Find the
More informationFPGA Implementation of a Predictive Controller
FPGA Implementation of a Predictive Controller SIAM Conference on Optimization 2011, Darmstadt, Germany Minisymposium on embedded optimization Juan L. Jerez, George A. Constantinides and Eric C. Kerrigan
More informationEECS150 - Digital Design Lecture 18 - Counters
EECS150 - Digital Design Lecture 18 - Counters October 24, 2002 John Wawrzynek Fall 2002 EECS150 - Lec18-counters Page 1 Counters Special sequential circuits (FSMs) that sequence though a set outputs.
More informationEECS150 - Digital Design Lecture 18 - Counters
EECS50 - Digital Design Lecture 8 - Counters October 24, 2002 John Wawrzynek Fall 2002 EECS50 - Lec8-counters Page Counters Special sequential circuits (FSMs) that sequence though a set outputs. Examples:
More informationProblem Set (T) If A is an m n matrix, B is an n p matrix and D is a p s matrix, then show
MTH 0: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur Problem Set Problems marked (T) are for discussions in Tutorial sessions (T) If A is an m n matrix,
More informationL16: Power Dissipation in Digital Systems. L16: Spring 2007 Introductory Digital Systems Laboratory
L16: Power Dissipation in Digital Systems 1 Problem #1: Power Dissipation/Heat Power (Watts) 100000 10000 1000 100 10 1 0.1 4004 80088080 8085 808686 386 486 Pentium proc 18KW 5KW 1.5KW 500W 1971 1974
More informationFPGA Realization of Low Register Systolic All One-Polynomial Multipliers Over GF (2 m ) and their Applications in Trinomial Multipliers
Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2016 FPGA Realization of Low Register Systolic All One-Polynomial Multipliers Over GF (2 m ) and their
More informationEnergy Delay Optimization
EE M216A.:. Fall 21 Lecture 8 Energy Delay Optimization Prof. Dejan Marković ee216a@gmail.com Some Common Questions Is sizing better than V DD for energy reduction? What are the optimal values of gate
More informationRegister Transfer Level (RTL) Design based on Vahid chap. 5
CSE4: Components and Design Techniques for Digital Systems Register Transfer Level (RTL) Design based on Vahid chap. 5 Tajana Simunic Rosing RTL Design Method RTL Design example: Laser-Based Distance Measurer
More informationFinal Review Accelerated Advanced Algebra
Name: ate: 1. What are the factors of z + z 2 + 25z + 25? 5. Factor completely: (7x + 2) 2 6 (z + 1)(z + 5)(z 5) (z 1)(z + 5i) 2 (49x + 1)(x 8) (7x 4)(7x + 8) (7x + 4)(7x 8) (7x + 4)(x 9) (z 1)(z + 5i)(z
More informationCOVER SHEET: Problem#: Points
EEL 4712 Midterm 3 Spring 2017 VERSION 1 Name: UFID: Sign here to give permission for your test to be returned in class, where others might see your score: IMPORTANT: Please be neat and write (or draw)
More informationMatrix Arithmetic. j=1
An m n matrix is an array A = Matrix Arithmetic a 11 a 12 a 1n a 21 a 22 a 2n a m1 a m2 a mn of real numbers a ij An m n matrix has m rows and n columns a ij is the entry in the i-th row and j-th column
More informationEECS150 - Digital Design Lecture 17 - Sequential Circuits 3 (Counters)
EECS150 - Digital Design Lecture 17 - Sequential Circuits 3 (Counters) March 19&21, 2002 John Wawrzynek Spring 2002 EECS150 - Lec13-seq3 version 2 Page 1 Counters Special sequential circuits (FSMs) that
More informationWe could express the left side as a sum of vectors and obtain the Vector Form of a Linear System: a 12 a x n. a m2
Week 22 Equations, Matrices and Transformations Coefficient Matrix and Vector Forms of a Linear System Suppose we have a system of m linear equations in n unknowns a 11 x 1 + a 12 x 2 + + a 1n x n b 1
More informationPhysicsAndMathsTutor.com
Question Answer Marks Guidance x x x mult throughout by (x + )(x ) or combining fractions and mult up oe (can retain denominator throughout). Condone a single computational error provided that there xx)
More informationDSP Design Lecture 7. Unfolding cont. & Folding. Dr. Fredrik Edman.
SP esign Lecture 7 Unfolding cont. & Folding r. Fredrik Edman fredrik.edman@eit.lth.se Unfolding Unfolding creates a program with more than one iteration, J=unfolding factor Unfolding is a structured way
More informationMatrices: 2.1 Operations with Matrices
Goals In this chapter and section we study matrix operations: Define matrix addition Define multiplication of matrix by a scalar, to be called scalar multiplication. Define multiplication of two matrices,
More informationEECS150 - Digital Design Lecture 22 - Arithmetic Blocks, Part 1
EECS150 - igital esign Lecture 22 - Arithmetic Blocks, Part 1 April 10, 2011 John Wawrzynek Spring 2011 EECS150 - Lec23-arith1 Page 1 Each cell: r i = a i XOR b i XOR c in Carry-ripple Adder Revisited
More informationReview: Designing with FSM. EECS Components and Design Techniques for Digital Systems. Lec09 Counters Outline.
Review: Designing with FSM EECS 150 - Components and Design Techniques for Digital Systems Lec09 Counters 9-28-04 David Culler Electrical Engineering and Computer Sciences University of California, Berkeley
More informationA = 3 B = A 1 1 matrix is the same as a number or scalar, 3 = [3].
Appendix : A Very Brief Linear ALgebra Review Introduction Linear Algebra, also known as matrix theory, is an important element of all branches of mathematics Very often in this course we study the shapes
More informationFSM model for sequential circuits
1 FSM model for sequential circuits The mathematical model of a sequential circuit is called finite-state machine. FSM is fully characterized by: S Finite set of states ( state ~ contents of FFs) I Finite
More informationEE141. Lecture 28 Multipliers. Lecture #20. Project Phase 2 Posted. Sign up for one of three project goals today
EE141-pring 2008 igital Integrated ircuits Lecture 28 Multipliers 1 Announcements Project Phase 2 Posted ign up for one of three project goals today Graded Phase 1 and Midterm 2 will be returned next Fr
More informationMULTIPLYING TRINOMIALS
Name: Date: 1 Math 2 Variable Manipulation Part 4 Polynomials B MULTIPLYING TRINOMIALS Multiplying trinomials is the same process as multiplying binomials except for there are more terms to multiply than
More informationA VERY BRIEF LINEAR ALGEBRA REVIEW for MAP 5485 Introduction to Mathematical Biophysics Fall 2010
A VERY BRIEF LINEAR ALGEBRA REVIEW for MAP 5485 Introduction to Mathematical Biophysics Fall 00 Introduction Linear Algebra, also known as matrix theory, is an important element of all branches of mathematics
More informationFormula for the inverse matrix. Cramer s rule. Review: 3 3 determinants can be computed expanding by any row or column
Math 20F Linear Algebra Lecture 18 1 Determinants, n n Review: The 3 3 case Slide 1 Determinants n n (Expansions by rows and columns Relation with Gauss elimination matrices: Properties) Formula for the
More informationReduced-Area Constant-Coefficient and Multiple-Constant Multipliers for Xilinx FPGAs with 6-Input LUTs
Article Reduced-Area Constant-Coefficient and Multiple-Constant Multipliers for Xilinx FPGAs with 6-Input LUTs E. George Walters III Department of Electrical and Computer Engineering, Penn State Erie,
More informationLesson 3.5 Exercises, pages
Lesson 3.5 Exercises, pages 232 238 A 4. Calculate the value of the discriminant for each quadratic equation. a) 5x 2-9x + 4 = 0 b) 3x 2 + 7x - 2 = 0 In b 2 4ac, substitute: In b 2 4ac, substitute: a 5,
More informationCMPEN 411 VLSI Digital Circuits Spring Lecture 19: Adder Design
CMPEN 411 VLSI Digital Circuits Spring 2011 Lecture 19: Adder Design [Adapted from Rabaey s Digital Integrated Circuits, Second Edition, 2003 J. Rabaey, A. Chandrakasan, B. Nikolic] Sp11 CMPEN 411 L19
More informationGaussian Elimination -(3.1) b 1. b 2., b. b n
Gaussian Elimination -() Consider solving a given system of n linear equations in n unknowns: (*) a x a x a n x n b where a ij and b i are constants and x i are unknowns Let a n x a n x a nn x n a a a
More informationAPPENDIX : PARTIAL FRACTIONS
APPENDIX : PARTIAL FRACTIONS Appendix : Partial Fractions Given the expression x 2 and asked to find its integral, x + you can use work from Section. to give x 2 =ln( x 2) ln( x + )+c x + = ln k x 2 x+
More informationSequential Synchronous Circuit Analysis
Sequential Synchronous Circuit Analysis General Model Current State at time (t) is stored in an array of flip-flops. Next State at time (t+1) is a Boolean function of State and Inputs. Outputs at time
More informationSection 8.3 Partial Fraction Decomposition
Section 8.6 Lecture Notes Page 1 of 10 Section 8.3 Partial Fraction Decomposition Partial fraction decomposition involves decomposing a rational function, or reversing the process of combining two or more
More informationSimple Instruction-Pipelining. Pipelined Harvard Datapath
6.823, L8--1 Simple ruction-pipelining Laboratory for Computer Science M.I.T. http://www.csg.lcs.mit.edu/6.823 Pipelined Harvard path 6.823, L8--2. I fetch decode & eg-fetch execute memory Clock period
More informationCyclic codes. Vahid Meghdadi Reference: Error Correction Coding by Todd K. Moon. February 2008
Cyclic codes Vahid Meghdadi Reference: Error Correction Coding by Todd K. Moon February 2008 1 Definitions Definition 1. A ring < R, +,. > is a set R with two binary operation + (addition) and. (multiplication)
More informationLesson 12.7: Sequences and Series
Lesson 12.7: Sequences and Series May 30 7:11 AM Sequences Definition: A sequence is a set of numbers in a specific order. 2, 5, 8,. is an example of a sequence. Note: A sequence may have either a finite
More informationFind two positive factors of 24 whose sum is 10. Make an organized list.
9.5 Study Guide For use with pages 582 589 GOAL Factor trinomials of the form x 2 1 bx 1 c. EXAMPLE 1 Factor when b and c are positive Factor x 2 1 10x 1 24. Find two positive factors of 24 whose sum is
More informationPhys 201. Matrices and Determinants
Phys 201 Matrices and Determinants 1 1.1 Matrices 1.2 Operations of matrices 1.3 Types of matrices 1.4 Properties of matrices 1.5 Determinants 1.6 Inverse of a 3 3 matrix 2 1.1 Matrices A 2 3 7 =! " 1
More informationElementary Differential Equations, Section 2 Prof. Loftin: Practice Test Problems for Test Find the radius of convergence of the power series
Elementary Differential Equations, Section 2 Prof. Loftin: Practice Test Problems for Test 2 SOLUTIONS 1. Find the radius of convergence of the power series Show your work. x + x2 2 + x3 3 + x4 4 + + xn
More informationLinear Equations in Linear Algebra
1 Linear Equations in Linear Algebra 1.7 LINEAR INDEPENDENCE LINEAR INDEPENDENCE Definition: An indexed set of vectors {v 1,, v p } in n is said to be linearly independent if the vector equation x x x
More informationNECESSARY AND SUFFICIENT CONDITIONS FOR STATE-SPACE NETWORK REALIZATION. Philip E. Paré Masters Thesis Defense
NECESSARY AND SUFFICIENT CONDITIONS FOR STATE-SPACE NETWORK REALIZATION Philip E. Paré Masters Thesis Defense INTRODUCTION MATHEMATICAL MODELING DYNAMIC MODELS A dynamic model has memory DYNAMIC MODELS
More informationInteger Multipliers 1
Integer Multipliers Multipliers must have circuit in most DS applications variety of multipliers exists that can be chosen based on their performance Serial, Serial/arallel,Shift and dd, rray, ooth, Wallace
More informationreview To find the coefficient of all the terms in 15ab + 60bc 17ca: Coefficient of ab = 15 Coefficient of bc = 60 Coefficient of ca = -17
1. Revision Recall basic terms of algebraic expressions like Variable, Constant, Term, Coefficient, Polynomial etc. The coefficients of the terms in 4x 2 5xy + 6y 2 are Coefficient of 4x 2 is 4 Coefficient
More informationFall Inverse of a matrix. Institute: UC San Diego. Authors: Alexander Knop
Fall 2017 Inverse of a matrix Authors: Alexander Knop Institute: UC San Diego Row-Column Rule If the product AB is defined, then the entry in row i and column j of AB is the sum of the products of corresponding
More informationMatrices and Linear Algebra
Contents Quantitative methods for Economics and Business University of Ferrara Academic year 2017-2018 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2 3 4 5 Contents 1 Basics 2
More information5.3. Polynomials and Polynomial Functions
5.3 Polynomials and Polynomial Functions Polynomial Vocabulary Term a number or a product of a number and variables raised to powers Coefficient numerical factor of a term Constant term which is only a
More informationMatrix Operations. Linear Combination Vector Algebra Angle Between Vectors Projections and Reflections Equality of matrices, Augmented Matrix
Linear Combination Vector Algebra Angle Between Vectors Projections and Reflections Equality of matrices, Augmented Matrix Matrix Operations Matrix Addition and Matrix Scalar Multiply Matrix Multiply Matrix
More informationEE141- Spring 2004 Digital Integrated Circuits
EE141- pring 2004 Digital Integrated ircuits Lecture 19 Dynamic Logic - Adders (that is wrap-up) 1 Administrative tuff Hw 6 due on Th No lab this week Midterm 2 next week Project 2 to be launched week
More informationDetermining a span. λ + µ + ν = x 2λ + 2µ 10ν = y λ + 3µ 9ν = z.
Determining a span Set V = R 3 and v 1 = (1, 2, 1), v 2 := (1, 2, 3), v 3 := (1 10, 9). We want to determine the span of these vectors. In other words, given (x, y, z) R 3, when is (x, y, z) span(v 1,
More informationMS 2001: Test 1 B Solutions
MS 2001: Test 1 B Solutions Name: Student Number: Answer all questions. Marks may be lost if necessary work is not clearly shown. Remarks by me in italics and would not be required in a test - J.P. Question
More informationMatrix Algebra 2.1 MATRIX OPERATIONS Pearson Education, Inc.
2 Matrix Algebra 2.1 MATRIX OPERATIONS MATRIX OPERATIONS m n If A is an matrixthat is, a matrix with m rows and n columnsthen the scalar entry in the ith row and jth column of A is denoted by a ij and
More informationName: ID# a) Complete the state transition table for the aforementioned circuit
UNIVERSITY OF CALIFORNIA Department of Electrical Engineering and Computer Sciences EECS150 Fall 2001 Prof. Subramanian Final Examination 1) You are to design a sequential circuit with two JK FFs A and
More informationMath 60. Rumbos Spring Solutions to Assignment #17
Math 60. Rumbos Spring 2009 1 Solutions to Assignment #17 a b 1. Prove that if ad bc 0 then the matrix A = is invertible and c d compute A 1. a b Solution: Let A = and assume that ad bc 0. c d First consider
More informationLeast Period of Linear Recurring Sequences over a Finite Field
Degree Project Least Period of Linear Recurring Sequences over a Finite Field 2012-02-29 Author: Sajid Hanif Subject: Mathematics Level: Master Course code: 5MA12E Abstract This thesis deals with fundamental
More informationSection 9.2: Matrices. Definition: A matrix A consists of a rectangular array of numbers, or elements, arranged in m rows and n columns.
Section 9.2: Matrices Definition: A matrix A consists of a rectangular array of numbers, or elements, arranged in m rows and n columns. That is, a 11 a 12 a 1n a 21 a 22 a 2n A =...... a m1 a m2 a mn A
More informationCPSC 313 Introduction to Computability
CPSC 313 Introduction to Computability Grammars in Chomsky Normal Form (Cont d) (Sipser, pages 109-111 (3 rd ed) and 107-109 (2 nd ed)) Renate Scheidler Fall 2018 Chomsky Normal Form A context-free grammar
More informationCHAPTER 1 POLYNOMIALS
1 CHAPTER 1 POLYNOMIALS 1.1 Removing Nested Symbols of Grouping Simplify. 1. 4x + 3( x ) + 4( x + 1). ( ) 3x + 4 5 x 3 + x 3. 3 5( y 4) + 6 y ( y + 3) 4. 3 n ( n + 5) 4 ( n + 8) 5. ( x + 5) x + 3( x 6)
More informationChapter 5. Digital Design and Computer Architecture, 2 nd Edition. David Money Harris and Sarah L. Harris. Chapter 5 <1>
Chapter 5 Digital Design and Computer Architecture, 2 nd Edition David Money Harris and Sarah L. Harris Chapter 5 Chapter 5 :: Topics Introduction Arithmetic Circuits umber Systems Sequential Building
More informationUMBC. At the system level, DFT includes boundary scan and analog test bus. The DFT techniques discussed focus on improving testability of SAFs.
Overview Design for testability(dft) makes it possible to: Assure the detection of all faults in a circuit. Reduce the cost and time associated with test development. Reduce the execution time of performing
More informationENGR-1100 Introduction to Engineering Analysis. Lecture 21
ENGR-1100 Introduction to Engineering Analysis Lecture 21 Lecture outline Procedure (algorithm) for finding the inverse of invertible matrix. Investigate the system of linear equation and invertibility
More informationPermutations and Polynomials Sarah Kitchen February 7, 2006
Permutations and Polynomials Sarah Kitchen February 7, 2006 Suppose you are given the equations x + y + z = a and 1 x + 1 y + 1 z = 1 a, and are asked to prove that one of x,y, and z is equal to a. We
More informationVLSI Design. [Adapted from Rabaey s Digital Integrated Circuits, 2002, J. Rabaey et al.] ECE 4121 VLSI DEsign.1
VLSI Design Adder Design [Adapted from Rabaey s Digital Integrated Circuits, 2002, J. Rabaey et al.] ECE 4121 VLSI DEsign.1 Major Components of a Computer Processor Devices Control Memory Input Datapath
More informationLinear Algebra and Matrix Inversion
Jim Lambers MAT 46/56 Spring Semester 29- Lecture 2 Notes These notes correspond to Section 63 in the text Linear Algebra and Matrix Inversion Vector Spaces and Linear Transformations Matrices are much
More informationDSP Design Lecture 5. Dr. Fredrik Edman.
SP esign SP esign Lecture 5 Retiming r. Fredrik Edman fredrik.edman@eit.lth.se Fredrik Edman, ept. of Electrical and Information Technology, Lund University, Sweden-www.eit.lth.se SP esign Repetition Critical
More informationECE 5775 (Fall 17) High-Level Digital Design Automation. Scheduling: Exact Methods
ECE 5775 (Fall 17) High-Level Digital Design Automation Scheduling: Exact Methods Announcements Sign up for the first student-led discussions today One slot remaining Presenters for the 1st session will
More informationLinear Systems and Matrices
Department of Mathematics The Chinese University of Hong Kong 1 System of m linear equations in n unknowns (linear system) a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.......
More informationLogic and Computer Design Fundamentals. Chapter 8 Sequencing and Control
Logic and Computer Design Fundamentals Chapter 8 Sequencing and Control Datapath and Control Datapath - performs data transfer and processing operations Control Unit - Determines enabling and sequencing
More informationCSE477 VLSI Digital Circuits Fall Lecture 20: Adder Design
CSE477 VLSI Digital Circuits Fall 22 Lecture 2: Adder Design Mary Jane Irwin ( www.cse.psu.edu/~mji ) www.cse.psu.edu/~cg477 [Adapted from Rabaey s Digital Integrated Circuits, 22, J. Rabaey et al.] CSE477
More informationWarm-Up. Use long division to divide 5 into
Warm-Up Use long division to divide 5 into 3462. 692 5 3462-30 46-45 12-10 2 Warm-Up Use long division to divide 5 into 3462. Divisor 692 5 3462-30 46-45 12-10 2 Quotient Dividend Remainder Warm-Up Use
More informationSimple Instruction-Pipelining. Pipelined Harvard Datapath
6.823, L8--1 Simple ruction-pipelining Updated March 6, 2000 Laboratory for Computer Science M.I.T. http://www.csg.lcs.mit.edu/6.823 Pipelined Harvard path 6.823, L8--2. fetch decode & eg-fetch execute
More informationFunctions and Equations
Canadian Mathematics Competition An activity of the Centre for Education in Mathematics and Computing, University of Waterloo, Waterloo, Ontario Euclid eworkshop # Functions and Equations c 006 CANADIAN
More informationMathematics I. Exercises with solutions. 1 Linear Algebra. Vectors and Matrices Let , C = , B = A = Determine the following matrices:
Mathematics I Exercises with solutions Linear Algebra Vectors and Matrices.. Let A = 5, B = Determine the following matrices: 4 5, C = a) A + B; b) A B; c) AB; d) BA; e) (AB)C; f) A(BC) Solution: 4 5 a)
More informationLoad. Load. Load 1 0 MUX B. MB select. Bus A. A B n H select S 2:0 C S. G select 4 V C N Z. unit (ALU) G. Zero Detect.
9- Write D data Load eable A address A select B address B select Load R 2 2 Load Load R R2 UX 2 3 UX 2 3 2 3 Decoder D address 2 Costat i Destiatio select 28 Pearso Educatio, Ic.. orris ao & Charles R.
More informationA matrix over a field F is a rectangular array of elements from F. The symbol
Chapter MATRICES Matrix arithmetic A matrix over a field F is a rectangular array of elements from F The symbol M m n (F ) denotes the collection of all m n matrices over F Matrices will usually be denoted
More informationAlg. I Mid-Term Review. A x > 3 2. C x > 3 D x < 3. A x > 7 B x < C x < 6 D x > A x > 2 B x < 2. C x > 2 D x < 2
lg. I Mid-Term Review Name: ate: 1. Which sentence is an example of the distributive property? ab = ba a(bc) = (ab)c a(b + c) = ab + ac a 1 = a 5. The inequality 3x + 2 > x + 8 is equivalent to x > 3 2
More informationPerformance, Power & Energy. ELEC8106/ELEC6102 Spring 2010 Hayden Kwok-Hay So
Performance, Power & Energy ELEC8106/ELEC6102 Spring 2010 Hayden Kwok-Hay So Recall: Goal of this class Performance Reconfiguration Power/ Energy H. So, Sp10 Lecture 3 - ELEC8106/6102 2 PERFORMANCE EVALUATION
More informationand the compositional inverse when it exists is A.
Lecture B jacques@ucsd.edu Notation: R denotes a ring, N denotes the set of sequences of natural numbers with finite support, is a generic element of N, is the infinite zero sequence, n 0 R[[ X]] denotes
More information