f31 book arith pres pt1

Views:
 
Category: Entertainment
     
 

Presentation Description

No description available.

Comments

Presentation Transcript

Part I Number Representation: 

Part I Number Representation

About This Presentation: 

About This Presentation This presentation is intended to support the use of the textbook Computer Arithmetic: Algorithms and Hardware Designs (Oxford University Press, 2000, ISBN 0-19-512583-5). It is updated regularly by the author as part of his teaching of the graduate course ECE 252B, Computer Arithmetic, at the University of California, Santa Barbara. Instructors can use these slides freely in classroom teaching and for other educational purposes. Unauthorized uses are strictly prohibited. © Behrooz Parhami

I Background and Motivation: 

I Background and Motivation Number representation arguably the most important topic: Effects on system compatibility and ease of arithmetic 2’s-complement, redundant, residue number systems Limits of fast arithmetic Floating-point numbers to be covered in Chapter 17

1 Numbers and Arithmetic: 

1 Numbers and Arithmetic Chapter Goals Define scope and provide motivation Set the framework for the rest of the book Review positional fixed-point numbers Chapter Highlights What goes on inside your calculator? Ways of encoding numbers in k bits Radices and digit sets: conventional, exotic Conversion from one system to another

Numbers and Arithmetic: Topics: 

Numbers and Arithmetic: Topics

1.1 What is Computer Arithmetic?: 

1.1 What is Computer Arithmetic? Pentium Division Bug (1994-95): Pentium’s radix-4 SRT algorithm occasionally gave incorrect quotient First noted in 1994 by T. Nicely who computed sums of reciprocals of twin primes: 1/5 + 1/7 + 1/11 + 1/13 + . . . + 1/p + 1/(p + 2) + . . . Worst-case example of division error in Pentium:

Top Ten Intel Slogans for the Pentium: 

Top Ten Intel Slogans for the Pentium Humor, circa 1995 9.999 997 325 It’s a FLAW, dammit, not a bug 8.999 916 336 It’s close enough, we say so 7.999 941 461 Nearly 300 correct opcodes 6.999 983 153 You don’t need to know what’s inside 5.999 983 513 Redefining the PC –– and math as well 4.999 999 902 We fixed it, really 3.999 824 591 Division considered harmful 2.999 152 361 Why do you think it’s called “floating” point? 1.999 910 351 We’re looking for a few good flaws 0.999 999 999 The errata inside

Aspects of, and Topics in, Computer Arithmetic: 

Aspects of, and Topics in, Computer Arithmetic Fig. 1.1 The scope of computer arithmetic. Hardware (our focus in this book) Software ––––––––––––––––––––––––––––––––––––––––––––––––– –––––––––––––––––––––––––––––––––––– Design of efficient digital circuits for Numerical methods for solving primitive and other arithmetic operations systems of linear equations, such as +, –, , , , log, sin, cos partial differential equations, etc. Issues: Algorithms Issues: Algorithms Error analysis Error analysis Speed/cost trade-offs Computational complexity Hardware implementation Programming Testing, verification Testing, verification General-purpose Special-purpose –––––––––––––––––––––– ––––––––––––––––––––––– Flexible data paths Tailored to Fast primitive applications like: operations like Digital filtering +, –, , ,  Image processing Benchmarking Radar tracking

1.2 A Motivating Example: 

Using a calculator with √, x2, and xy functions, compute: u = √√ … √ 2 = 1.000 677 131 “1024th root of 2” v = 21/1024 = 1.000 677 131 Save u and v; If you can’t save, recompute values when needed x = (((u2)2)...)2 = 1.999 999 963 x' = u1024 = 1.999 999 973 y = (((v2)2)...)2 = 1.999 999 983 y' = v1024 = 1.999 999 994 Perhaps v and u are not really the same value w = v – u = 1  10–11 Nonzero due to hidden digits (u – 1)  1000 = 0.677 130 680 [Hidden ... (0) 68] (v – 1)  1000 = 0.677 130 690 [Hidden ... (0) 69] 1.2 A Motivating Example

Finite Precision Can Lead to Disaster: 

Finite Precision Can Lead to Disaster Example: Failure of Patriot Missile (1991 Feb. 25) Source http://www.math.psu.edu/dna/455.f96/disasters.html American Patriot Missile battery in Dharan, Saudi Arabia, failed to intercept incoming Iraqi Scud missile The Scud struck an American Army barracks, killing 28 Cause, per GAO/IMTEC-92-26 report: “software problem” (inaccurate calculation of the time since boot) Problem specifics: Time in tenths of second as measured by the system’s internal clock was multiplied by 1/10 to get the time in seconds Internal registers were 24 bits wide 1/10 = 0.0001 1001 1001 1001 1001 100 (chopped to 24 b) Error ≈ 0.1100 1100  2–23 ≈ 9.5  10–8 Error in 100-hr operation period ≈ 9.5  10 –8  100  60  60  10 = 0.34 s Distance traveled by Scud = (0.34 s)  (1676 m/s) ≈ 570 m

Finite Range Can Lead to Disaster: 

Finite Range Can Lead to Disaster Example: Explosion of Ariane Rocket (1996 June 4) Source http://www.math.psu.edu/dna/455.f96/disasters.html Unmanned Ariane 5 rocket of the European Space Agency veered off its flight path, broke up, and exploded only 30 s after lift-off (altitude of 3700 m) The $500 million rocket (with cargo) was on its first voyage after a decade of development costing $7 billion Cause: “software error in the inertial reference system” Problem specifics: A 64 bit floating point number relating to the horizontal velocity of the rocket was being converted to a 16 bit signed integer An SRI* software exception arose during conversion because the 64-bit floating point number had a value greater than what could be represented by a 16-bit signed integer (max 32 767) *SRI = Système de Référence Inertielle or Inertial Reference System

1.3 Numbers and Their Encodings: 

1.3 Numbers and Their Encodings Some 4-bit number representation formats

Encoding Numbers in 4 Bits: 

Encoding Numbers in 4 Bits Fig. 1.2 Some of the possible ways of assigning 16 distinct codes to represent numbers.

1.4 Fixed-Radix Positional Number Systems: 

1.4 Fixed-Radix Positional Number Systems ( xk–1xk–2 . . . x1x0 . x–1x–2 . . . x–l )r = xi r i One can generalize to: Arbitrary radix (not necessarily integer, positive, constant) Arbitrary digit set, usually {–a, –a+1, . . . , b–1, b} = [–a, b] Example 1.1. Balanced ternary number system: Radix r = 3, digit set = [–1, 1] Example 1.2. Negative-radix number systems: Radix –r, r  2, digit set = [0, r – 1]   The special case with radix –2 and digit set [0, 1] is known as the negabinary number system

More Examples of Number Systems: 

More Examples of Number Systems Example 1.3. Digit set [–4, 5] for r = 10: (3 –1 5)ten represents 295 = 300 – 10 + 5 Example 1.4. Digit set [–7, 7] for r = 10: (3 –1 5)ten = (3 0 –5)ten = (1 –7 0 –5)ten Example 1.7. Quater-imaginary number system: radix r = 2j, digit set [0, 3]

1.5 Number Radix Conversion: 

1.5 Number Radix Conversion Radix conversion, using arithmetic in the old radix r Convenient when converting from r = 10 u = w . v = ( xk–1xk–2 . . . x1x0 . x–1x–2 . . . x–l )r Old = ( XK–1XK–2 . . . X1X0 . X–1X–2 . . . X–L )R New Radix conversion, using arithmetic in the new radix R Convenient when converting to R = 10 Example: (31)eight = (25)ten 31 Oct. = 25 Dec. Halloween = Xmas

Radix Conversion: Old-Radix Arithmetic: 

Radix Conversion: Old-Radix Arithmetic Converting whole part w: (105)ten = (?)five Repeatedly divide by five Quotient Remainder 105 0 21 1 4 4 0 Therefore, (105)ten = (410)five Converting fractional part v: (105.486)ten = (410.?)five Repeatedly multiply by five Whole Part Fraction .486 2 .430 2 .150 0 .750 3 .750 3 .750 Therefore, (105.486)ten  (410.22033)five

Radix Conversion: New-Radix Arithmetic: 

Radix Conversion: New-Radix Arithmetic Converting whole part w: (22033)five = (?)ten ((((2  5) + 2)  5 + 0)  5 + 3)  5 + 3 |-----| : : : : 10 : : : : |-----------| : : : 12 : : : |---------------------| : : 60 : : |-------------------------------| : 303 : |-----------------------------------------| 1518 Converting fractional part v: (410.22033)five = (105.?)ten (0.22033)five  55 = (22033)five = (1518)ten 1518 / 55 = 1518 / 3125 = 0.48576 Therefore, (410.22033)five = (105.48576)ten Horner’s rule is also applicable: Proceed from right to left and use division instead of multiplication Horner’s rule or formula

Horner’s Rule for Fractions: 

Horner’s Rule for Fractions Converting fractional part v: (0.22033)five = (?)ten (((((3 / 5) + 3) / 5 + 0) / 5 + 2) / 5 + 2) / 5 |-----| : : : : 0.6 : : : : |-----------| : : : 3.6 : : : |---------------------| : : 0.72 : : |-------------------------------| : 2.144 : |-----------------------------------------| 2.4288 |-----------------------------------------------| 0.48576 Horner’s rule or formula Fig. 1.3 Horner’s rule used to convert (0.220 33)five to decimal.

1.6 Classes of Number Representations: 

1.6 Classes of Number Representations Integers (fixed-point), unsigned: Chapter 1 Integers (fixed-point), signed Signed-magnitude, biased, complement: Chapter 2 Signed-digit, including carry/borrow-save: Chapter 3 (but the key point of Chapter 3 is using redundancy for faster arithmetic, not how to represent signed values) Residue number system: Chapter 4 (again, the key to Chapter 4 is use of parallelism for faster arithmetic, not how to represent signed values) Real numbers, floating-point: Chapter 17 Part V deals with real arithmetic Real numbers, exact: Chapter 20 Continued-fraction, slash, . . . For the most part you need: - The 2’s complement format - Carry-save representation - ANSI/IEEE FLP format However, knowing the rest of the material (including RNS) provides you with more options when designing custom and special-purpose hardware systems

2 Representing Signed Numbers: 

2 Representing Signed Numbers Chapter Goals Learn different encodings of the sign info Discuss implications for arithmetic design Chapter Highlights Using sign bit, biasing, complementation Properties of 2’s-complement numbers Signed vs unsigned arithmetic Signed numbers, positions, or digits

Representing Signed Numbers: Topics: 

Representing Signed Numbers: Topics

2.1 Signed-Magnitude Representation: 

2.1 Signed-Magnitude Representation Fig. 2.1 Four-bit signed-magnitude number representation system for integers. -

Signed-Magnitude Adder: 

Signed-Magnitude Adder Fig. 2.2 Adding signed-magnitude numbers using precomplementation and postcomplementation.

2.2 Biased Representations: 

2.2 Biased Representations Fig. 2.3 Four-bit biased integer number representation system with a bias of 8.

Arithmetic with Biased Numbers: 

Arithmetic with Biased Numbers Addition/subtraction of biased numbers x + y + bias = (x + bias) + (y + bias) – bias x – y + bias = (x + bias) – (y + bias) + bias A power-of-2 (or 2a – 1) bias simplifies addition/subtraction Comparison of biased numbers: Compare like ordinary unsigned numbers find true difference by ordinary subtraction We seldom perform arbitrary arithmetic on biased numbers Main application: Exponent field of floating-point numbers

2.3 Complement Representations: 

2.3 Complement Representations Fig. 2.4 Complement representation of signed integers.

Arithmetic with Complement Representations: 

Arithmetic with Complement Representations ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Desired Computation to be Correct result Overflow operation performed mod M with no overflow condition ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– (+x) + (+y) x + y x + y x + y > P (+x) + (–y) x + (M – y) x – y if y  x N/A M – (y – x) if y > x (–x) + (+y) (M – x) + y y – x if x  y N/A M – (x – y) if x > y (–x) + (–y) (M – x) + (M – y) M – (x + y) x + y > N ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Table 2.1 Addition in a complement number system with complementation constant M and range [–N, +P]

Example and Two Special Cases: 

Example and Two Special Cases Example -- complement system for fixed-point numbers: Complementation constant M = 12.000 Fixed-point number range [–6.000, +5.999] Represent –3.258 as 12.000 – 3.258 = 8.742 Auxiliary operations for complement representations complementation or change of sign (computing M – x) computations of residues mod M Thus, M must be selected to simplify these operations Two choices allow just this for fixed-point radix-r arithmetic with k whole digits and l fractional digits Radix complement M = rk Digit complement M = rk – ulp (aka diminished radix compl) ulp (unit in least position) stands for rl Allows us to forget about l, even for nonintegers

2.4 Two’s- and 1’s-Complement Numbers: 

2.4 Two’s- and 1’s-Complement Numbers Fig. 2.5 A 4-bit 2’s-complement number representation system for integers. Two’s complement = radix complement system for r = 2 M = 2k 2k – x = [(2k – ulp) – x] + ulp = xcompl + ulp Range of representable numbers in with k whole bits: from –2k–1 to 2k–1 – ulp

One’s-Complement Number Representation: 

One’s-Complement Number Representation Fig. 2.6 A 4-bit 1’s-complement number representation system for integers. One’s complement = digit complement (diminished radix complement) system for r = 2 M = 2k – ulp (2k – ulp) – x = xcompl Range of representable numbers in with k whole bits: from –2k–1 + ulp to 2k–1 – ulp

Some Details for 2’s- and 1’s Complement: 

Some Details for 2’s- and 1’s Complement Range/precision extension for 2’s-complement numbers . . . xk–1 xk–1 xk–1 xk–1 xk–2 . . . x1 x0 . x–1 x–2 . . . x–l 0 0 0 . . .  Sign extension  Sign LSD  Extension  bit Mod-(2k – ulp) operation needed in 1’s-complement arithmetic is done via end-around carry (x + y) – (2k – ulp) = (x – y – 2k) + ulp Connect cout to cin Mod-2k operation needed in 2’s-complement arithmetic is trivial: Simply drop the carry-out (subtract 2k if result is 2k or greater)

Which Complement System Is Better?: 

Which Complement System Is Better? ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Feature/Property Radix complement Digit complement ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Symmetry (P = N?) Possible for odd r Possible for even r (radices of practical interest are even) Unique zero? Yes No, there are two 0s Complementation Complement all digits Complement all digits and add ulp Mod-M addition Drop the carry-out End-around carry ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Table 2.2 Comparing radix- and digit-complement number representation systems

Why 2’s-Complement Is the Universal Choice: 

Why 2’s-Complement Is the Universal Choice Fig. 2.7 Adder/subtractor architecture for 2’s-complement numbers.

Signed-Magnitude vs 2’s-Complement: 

Signed-Magnitude vs 2’s-Complement Fig. 2.2

2.5 Direct and Indirect Signed Arithmetic: 

2.5 Direct and Indirect Signed Arithmetic Direct signed arithmetic is usually faster (not always) Indirect signed arithmetic can be simpler (not always); allows sharing of signed/unsigned hardware when both operation types are needed Fig. 2.8 Direct versus indirect operation on signed numbers.

2.6 Using Signed Positions or Signed Digits: 

2.6 Using Signed Positions or Signed Digits A key property of 2’s-complement numbers that facilitates direct signed arithmetic: Fig. 2.9 Interpreting a 2’s-complement number as having a negatively weighted most-significant digit. x = (1 0 1 0 0 1 1 0)two’s-compl –27 26 25 24 23 22 21 20 –128 + 32 + 4 + 2 = –90 Check: x = (1 0 1 0 0 1 1 0)two’s-compl –x = (0 1 0 1 1 0 1 0)two 27 26 25 24 23 22 21 20 64 + 16 + 8 + 2 = 90

Associating a Sign with Each Digit: 

Associating a Sign with Each Digit Fig. 2.10 Converting a standard radix-4 integer to a radix-4 integer with the nonstandard digit set [–1, 2]. Signed-digit representation: Digit set [-a, b] instead of [0, r – 1] Example: Radix-4 representation with digit set [-1, 2] rather than [0, 3]

Redundant Signed-Digit Representations: 

Redundant Signed-Digit Representations Fig. 2.11 Converting a standard radix-4 integer to a radix-4 integer with the nonstandard digit set [–2, 2]. Signed-digit representation: Digit set [-a, b], with r = a + b + 1 – r > 0 Example: Radix-4 representation with digit set [-2, 2] Here, the transfer does not propagate, so conversion is “carry-free”

3 Redundant Number Systems: 

3 Redundant Number Systems Chapter Goals Explore the advantages and drawbacks of using more than r digit values in radix r Chapter Highlights Redundancy eliminates long carry chains Redundancy takes many forms: trade-offs Conversions between redundant and nonredundant representations Redundancy used for end values too?

Redundant Number Systems: Topics: 

Redundant Number Systems: Topics

3.1 Coping with the Carry Problem: 

3.1 Coping with the Carry Problem Ways of dealing with the carry propagation problem: 1. Limit propagation to within a small number of bits (Chapters 3-4) 2. Detect end of propagation; don’t wait for worst case (Chapter 5) 3. Speed up propagation via lookahead etc. (Chapters 6-7) 4. Ideal: Eliminate carry propagation altogether! (Chapter 3)

Addition of Redundant Numbers: 

Addition of Redundant Numbers Fig. 3.1 Adding radix-10 numbers with digit set [0, 18]. Position sum decomposition [0, 36] = 10  [0, 2] + [0, 16] Absorption of transfer digit [0, 16] + [0, 2] = [0, 18]

Meaning of Carry-Free Addition: 

Meaning of Carry-Free Addition Fig. 3.2 Ideal and practical carry-free addition schemes. Interim sum at position i Transfer digit into position i Operand digits at position i

Redundancy Index: 

Redundancy Index Fig. 3.3 Adding radix-10 numbers with digit set [0, 11]. So, redundancy helps us achieve carry-free addition But how much redundancy is actually needed? Is [0, 11] enough for r = 10? Redundancy index r = a + b + 1 – r For example, 0 + 11 + 1 – 10 = 2

3.2 Redundancy in Computer Arithmetic: 

3.2 Redundancy in Computer Arithmetic Fig. 3.4 Addition of four binary numbers, with the sum obtained in stored-carry form. Oldest example of redundancy in computer arithmetic is the stored-carry representation (carry-save addition)

Hardware for Carry-Save Addition: 

Hardware for Carry-Save Addition Fig. 3.5 Using an array of independent binary full-adders to perform carry-save addition. Two-bit encoding for binary stored-carry digits used in this implementation: 0 represented as 0 0 1 represented as 0 1 or as 1 0 2 represented as 1 1 Because in carry-save addition, three binary numbers are reduced to two binary numbers, this process is sometimes referred to as 3-2 compression

Carry-Save Addition in Dot Notation: 

Carry-Save Addition in Dot Notation We sometimes find it convenient to use an extended dot notation, with heavy dots (●) for posibits and hollow dots (○) for negabits Eight-bit, 2’s-complement number ○ ● ● ● ● ● ● ● Negative-radix number ○ ● ○ ● ○ ● ○ ● BSD number with n, p encoding ○ ○ ○ ○ ○ ○ ○ ○ of the digit set [-1, 1] ● ● ● ● ● ● ● ● Fig. 9.3 From text on computer architecture (Parhami, Oxford/2005) 3-to-2 reduction 4-to-2 reduction

Example for the Use of Extended Dot Notation: 

Example for the Use of Extended Dot Notation 2’s-complement multiplicand ○ ● ● ● ● ● ● ● 2’s-complement multiplier ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ○ ● ● ● ● ● ● ● ● ○ ○ ○ ○ ○ ○ ○ ○ ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Multiplication of 2’s-complement numbers

3.3 Digit Sets and Digit-Set Conversions: 

3.3 Digit Sets and Digit-Set Conversions Example 3.1: Convert from digit set [0, 18] to [0, 9] in radix 10 11 9 17 10 12 18 18 = 10 (carry 1) + 8 11 9 17 10 13 8 13 = 10 (carry 1) + 3 11 9 17 11 3 8 11 = 10 (carry 1) + 1 11 9 18 1 3 8 18 = 10 (carry 1) + 8 11 10 8 1 3 8 10 = 10 (carry 1) + 0 12 0 8 1 3 8 12 = 10 (carry 1) + 2 1 2 0 8 1 3 8 Answer; all digits in [0, 9] Note: Conversion from redundant to nonredundant representation always involves carry propagation Thus, the process is sequential and slow

Conversion from Carry-Save to Binary: 

Conversion from Carry-Save to Binary Example 3.2: Convert from digit set [0, 2] to [0, 1] in radix 2 1 1 2 0 2 0 2 = 2 (carry 1) + 0 1 1 2 1 0 0 2 = 2 (carry 1) + 0 1 2 0 1 0 0 2 = 2 (carry 1) + 0 2 0 0 1 0 0 2 = 2 (carry 1) + 0 1 0 0 0 1 0 0 Answer; all digits in [0, 1] Another way: Decompose the carry-save number into two numbers and add them: 1 1 1 0 1 0 1st number: sum bits + 0 0 1 0 1 0 2nd number: carry bits –––––––––––––––––––––––––––––––––––––––– 1 0 0 0 1 0 0 Sum

Conversion Between Redundant Digit Sets: 

Conversion Between Redundant Digit Sets Example 3.3: Convert from digit set [0, 18] to [-6, 5] in radix 10 (same as Example 3.1, but with the target digit set signed and redundant) 11 9 17 10 12 18 18 = 20 (carry 2) – 2 11 9 17 10 14 -2 14 = 10 (carry 1) + 4 11 9 17 11 4 -2 11 = 10 (carry 1) + 1 11 9 18 1 4 -2 18 = 20 (carry 2) – 2 11 11 -2 1 4 -2 11 = 10 (carry 1) + 1 12 1 -2 1 4 -2 12 = 10 (carry 1) + 2 1 2 1 -2 1 4 -2 Answer; all digits in [-6, 5] On line 2, we could have written 14 = 20 (carry 2) – 6; this would have led to a different, but equivalent, representation In general, several representations may exist for a redundant digit set

Carry-Free Conversion to a Redundant Digit Set: 

Carry-Free Conversion to a Redundant Digit Set Example 3.4: Convert from digit set [0, 2] to [-1, 1] in radix 2 (same as Example 3.2, but with the target digit set signed and redundant) Carry-free conversion: 1 1 2 0 2 0 Carry-save number –1 –1 0 0 0 0 Interim digits in [–1, 0] 1 1 1 0 1 0 Transfer digits in [0, 1] –––––––––––––––––––––––––––––––––––––––– 1 0 0 0 1 0 0 Answer; all digits in [–1, 1] We rewrite 2 as 2 (carry 1) + 0, and 1 as 2 (carry 1) – 1 A carry of 1 is always absorbed by the interim digit that is in {-1, 0}

3.4 Generalized Signed-Digit Numbers: 

3.4 Generalized Signed-Digit Numbers Fig. 3.6 A taxonomy of redundant and non-redundant positional number systems. Radix r Digit set [–a, b] Requirement a + b + 1  r Redundancy index r = a + b + 1 – r

Encodings for Signed Digits: 

Encodings for Signed Digits Fig. 3.7 Four encodings for the BSD digit set [–1, 1]. xi 1 –1 0 –1 0 BSD representation of +6 s, v 01 11 00 11 00 Sign and value encoding 2’s-compl 01 10 00 10 00 2-bit 2’s-complement n, p 01 10 00 10 00 Negative & positive flags n, z, p 001 100 010 100 010 1-out-of-3 encoding Two of the encodings above can be shown in extended dot notation (● = posibit, ○ = negabit, ■ = doublebit, □ = negadoublebit) 2’s-compl □ □ □ □ □ ● ● ● ● ● n, p ○ ○ ○ ○ ○ ● ● ● ● ● These two encodings are nearly equivalent (left-shift the doublebits)

Hybrid Signed-Digit Numbers: 

Hybrid Signed-Digit Numbers Fig. 3.8 Example of addition for hybrid signed-digit numbers. Radix-8 GSD with digit set [-4,7]

3.5 Carry-Free Addition Algorithms: 

3.5 Carry-Free Addition Algorithms Carry-free addition of GSD numbers Compute the position sums pi = xi + yi Divide pi into a transfer ti+1 and interim sum wi = pi – rti+1 Add incoming transfers to get the sum digits si = wi + ti wi These constraints lead to: l  a / (r – 1) m  b / (r – 1)

Is Carry-Free Addition Always Applicable?: 

Is Carry-Free Addition Always Applicable? No: It requires one of the following two conditions a. r > 2, r  3 b. r > 2, r = 2, a  1, b  1 e.g., not [-1, 10] in radix 10 In other words, it is inapplicable for r = 2 Perhaps most useful case r = 1 e.g., carry-save r = 2 with a = 1 or b = 1 e.g., carry/borrow-save BSD fails on at least two criteria! Fortunately, in the latter cases, a limited-carry addition algorithm is always applicable

Limited-Carry Addition: 

Limited-Carry Addition Example: BSD addition 1 1 1 -1 0 1 0 -1 1 -1

Limited-Carry BSD Addition: 

Limited-Carry BSD Addition Fig. 3.12 Limited-carry addition of radix-2 numbers with digit set [–1, 1] using carry estimates. A position sum –1 is kept intact when the incoming transfer is in [0, 1], whereas it is rewritten as 1 with a carry of –1 for incoming transfer in [–1, 0]. This guarantees that ti  wi and thus –1  si  1.

3.6 Conversions and Support Functions: 

3.6 Conversions and Support Functions Example 3.10: Conversion from/to BSD to/from standard binary 1 -1 0 -1 0 BSD representation of +6 1 0 0 0 0 Positive part 0 1 0 1 0 Negative part 0 0 1 1 0 Difference = Conversion result The negative and positive parts above are particularly easy to obtain if the BSD number has the n, p encoding Conversion from redundant to nonredundant representation always requires full carry propagation Conversion from nonredundant to redundant is often trivial

Other Arithmetic Support Functions: 

  Other Arithmetic Support Functions Zero test: Zero has a unique code under some conditions Sign test: Needs carry propagation Overflow: May be real or apparent (result may be representable)

4 Residue Number Systems: 

4 Residue Number Systems Chapter Goals Study a way of encoding large numbers as a collection of smaller numbers to simplify and speed up some operations Chapter Highlights Moduli, range, arithmetic operations Many sets of moduli possible: tradeoffs Conversions between RNS and binary The Chinese remainder theorem Why are RNS applications limited?

Residue Number Systems: Topics: 

Residue Number Systems: Topics

4.1 RNS Representations and Arithmetic: 

4.1 RNS Representations and Arithmetic Chinese puzzle, 1500 years ago: What number has the remainders of 2, 3, and 2 when divided by 7, 5, and 3, respectively? Residues (akin to digits in positional systems) uniquely identify the number, hence they constitute a representation Pairwise relatively prime moduli: mk–1 > . . . > m1 > m0 The residue xi of x wrt the ith modulus mi (similar to a digit): xi = x mod mi = xmi RNS representation contains a list of k residues or digits: x = (2 | 3 | 2)RNS(7|5|3) Default RNS for this chapter: RNS(8 | 7 | 5 | 3)

RNS Dynamic Range: 

RNS Dynamic Range Product M of the k pairwise relatively prime moduli is the dynamic range M = mk–1  . . .  m1  m0 For RNS(8 | 7 | 5 | 3), M = 8  7  5  3 = 840 Negative numbers: Complement relative to M –xmi = M – xmi 21 = (5 | 0 | 1 | 0)RNS –21 = (8 – 5 | 0 | 5 – 1 | 0)RNS = (3 | 0 | 4 | 0)RNS Here are some example numbers in our default RNS(8 | 7 | 5 | 3): (0 | 0 | 0 | 0)RNS Represents 0 or 840 or . . . (1 | 1 | 1 | 1)RNS Represents 1 or 841 or . . . (2 | 2 | 2 | 2)RNS Represents 2 or 842 or . . . (0 | 1 | 3 | 2)RNS Represents 8 or 848 or . . . (5 | 0 | 1 | 0)RNS Represents 21 or 861 or . . . (0 | 1 | 4 | 1)RNS Represents 64 or 904 or . . . (2 | 0 | 0 | 2)RNS Represents –70 or 770 or . . . (7 | 6 | 4 | 2)RNS Represents –1 or 839 or . . . We can take the range of RNS(8|7|5|3) to be [-420, 419] or any other set of 840 consecutive integers

RNS as Weighted Representation: 

We will see later how the weights can be determined for a given RNS   RNS as Weighted Representation For RNS(8 | 7 | 5 | 3), the weights of the 4 positions are: 105 120 336 280 Example: (1 | 2 | 4 | 0)RNS represents the number 1051 + 1202 + 3364 + 2800840 = 1689840 = 9 For RNS(7 | 5 | 3), the weights of the 3 positions are: 15 21 70 Example -- Chinese puzzle: (2 | 3 | 2)RNS(7|5|3) represents the number 15  2 + 21  3 + 70  2105 = 233105 = 23

RNS Encoding and Arithmetic Operations: 

  RNS Encoding and Arithmetic Operations Fig. 4.1 Binary-coded format for RNS(8 | 7 | 5 | 3). Arithmetic in RNS(8 | 7 | 5 | 3) (5 | 5 | 0 | 2)RNS Represents x = +5 (7 | 6 | 4 | 2)RNS Represents y = –1 (4 | 4 | 4 | 1)RNS x + y : 5 + 78 = 4, 5 + 67 = 4, etc. (6 | 6 | 1 | 0)RNS x – y : 5 – 78 = 6, 5 – 67 = 6, etc. (alternatively, find –y and add to x) (3 | 2 | 0 | 1)RNS x  y : 5  78 = 3, 5  67 = 2, etc.

4.2 Choosing the RNS Moduli: 

4.2 Choosing the RNS Moduli Target range for our RNS: Decimal values [0, 100 000] Strategy 1: To minimize the largest modulus, and thus ensure high-speed arithmetic, pick prime numbers in sequence Pick m0 = 2, m1 = 3, m2 = 5, etc. After adding m5 = 13: RNS(13 | 11 | 7 | 5 | 3 | 2) M = 30 030 Inadequate RNS(17 | 13 | 11 | 7 | 5 | 3 | 2) M = 510 510 Too large RNS(17 | 13 | 11 | 7 | 3 | 2) M = 102 102 Just right! 5 + 4 + 4 + 3 + 2 + 1 = 19 bits Fine tuning: Combine pairs of moduli 2 & 13 (26) and 3 & 7 (21) RNS(26 | 21 | 17 | 11) M = 102 102

An Improved Strategy: 

An Improved Strategy Target range for our RNS: Decimal values [0, 100 000] Strategy 2: Improve strategy 1 by including powers of smaller primes before proceeding to the next larger prime RNS(22 | 3) M = 12 RNS(32 | 23 | 7 | 5) M = 2520 RNS(11 | 32 | 23 | 7 | 5) M = 27 720 RNS(13 | 11 | 32 | 23 | 7 | 5) M = 360 360 (remove one 3, combine 3 & 5) RNS(15 | 13 | 11 | 23 | 7) M = 120 120 4 + 4 + 4 + 3 + 3 = 18 bits Fine tuning: Maximize the size of the even modulus within the 4-bit limit RNS(24 | 13 | 11 | 32 | 7 | 5) M = 720 720 Too large We can now remove 5 or 7; not an improvement in this example

Low-Cost RNS Moduli: 

Low-Cost RNS Moduli Target range for our RNS: Decimal values [0, 100 000] Strategy 3: To simplify the modular reduction (mod mi) operations, choose only moduli of the forms 2a or 2a – 1, aka “low-cost moduli” RNS(2ak–1 | 2ak–2 – 1 | . . . | 2a1 – 1 | 2a0 – 1) We can have only one even modulus 2ai – 1 and 2aj – 1 are relatively prime iff ai and aj are relatively prime RNS(23 | 23–1 | 22–1) basis: 3, 2 M = 168 RNS(24 | 24–1 | 23–1) basis: 4, 3 M = 1680 RNS(25 | 25–1 | 23–1 | 22–1) basis: 5, 3, 2 M = 20 832 RNS(25 | 25–1 | 24–1 | 23–1) basis: 5, 4, 3 M = 104 160 Comparison RNS(15 | 13 | 11 | 23 | 7) 18 bits M = 120 120 RNS(25 | 25–1 | 24–1 | 23–1) 17 bits M = 104 160

4.3 Encoding and Decoding of Numbers: 

4.3 Encoding and Decoding of Numbers Conversion from binary/decimal to RNS ––––––––––––––––––––––––––––– i 2i 2i7 2i5 2i3 ––––––––––––––––––––––––––––– 0 1 1 1 1 1 2 2 2 2 2 4 4 4 1 3 8 1 3 2 4 16 2 1 1 5 32 4 2 2 6 64 1 4 1 7 128 2 3 2 8 256 4 1 1 9 512 1 2 2 ––––––––––––––––––––––––––––– Table 4.1 Residues of the first 10 powers of 2 Example 4.1: Represent the number y = (1010 0100)two = (164)ten in RNS(8 | 7 | 5 | 3) The mod-8 residue is easy to find x3 = y8 = (100)two = 4 We have y = 27+25+22; thus x2 = y7 = 2 + 4 + 47 = 3 x1 = y5 = 3 + 2 + 45 = 4 x0 = y3 = 2 + 2 + 13 = 2

Conversion from RNS to Mixed-Radix Form: 

  Conversion from RNS to Mixed-Radix Form MRS(mk–1 | . . . | m2 | m1 | m0) is a k-digit positional system with weights mk–2...m2m1m0 . . . m2m1m0 m1m0 m0 1 and digit sets [0, mk–1–1] . . . [0,m3–1] [0,m2–1] [0,m1–1] [0,m0–1] Example: (0 | 3 | 1 | 0)MRS(8|7|5|3) = 0105 + 315 + 13 + 01 = 48 RNS-to-MRS conversion problem: y = (xk–1 | . . . | x2 | x1 | x0)RNS = (zk–1 | . . . | z2 | z1 | z0)MRS MRS representation allows magnitude comparison and sign detection Example: 48 versus 45 (0 | 6 | 3 | 0)RNS vs (5 | 3 | 0 | 0)RNS (000 | 110 | 011 | 00)RNS vs (101 | 011 | 000 | 00)RNS Equivalent mixed-radix representations (0 | 3 | 1 | 0)MRS vs (0 | 3 | 0 | 0)MRS (000 | 011 | 001 | 00)MRS vs (000 | 011 | 000 | 00)MRS

Conversion from RNS to Binary/Decimal: 

  Conversion from RNS to Binary/Decimal Theorem 4.1 (The Chinese remainder theorem) x = (xk–1 | . . . | x2 | x1 | x0)RNS =  i Mi ai ximi M where Mi = M/mi and ai = Mi –1mi (multiplicative inverse of Mi wrt mi) Implementing CRT-based RNS-to-binary conversion x =  i Mi ai ximi M =  i fi(xi) M We can use a table to store the fi values –- i mi entries Table 4.2 Values needed in applying the Chinese remainder theorem to RNS(8 | 7 | 5 | 3) –––––––––––––––––––––––––––––– i mi xi Mi ai ximiM –––––––––––––––––––––––––––––– 3 8 0 0 1 105 2 210 3 315 . . . . . .

Intuitive Justification for CRT: 

  Intuitive Justification for CRT Puzzle: What number has the remainders of 2, 3, and 2 when divided by the numbers 7, 5, and 3, respectively? x = (2 | 3 | 2)RNS(7|5|3) = (?)ten (1 | 0 | 0)RNS(7|5|3) = multiple of 15 that is 1 mod 7 = 15 (0 | 1 | 0)RNS(7|5|3) = multiple of 21 that is 1 mod 5 = 21 (0 | 0 | 1)RNS(7|5|3) = multiple of 35 that is 1 mod 3 = 70 (2 | 3 | 2)RNS(7|5|3) = (2 | 0 | 0) + (0 | 3 | 0) + (0 | 0 | 2) = 2  (1 | 0 | 0) + 3  (0 | 1 | 0) + 2  (0 | 0 | 1) = 2  15 + 3  21 + 2  70 = 30 + 63 + 140 = 233 = 23 mod 105 Therefore, x = (23)ten

4.4 Difficult RNS Arithmetic Operations: 

4.4 Difficult RNS Arithmetic Operations Sign test and magnitude comparison are difficult Example: Of the following RNS(8 | 7 | 5 | 3) numbers: Which, if any, are negative? Which is the largest? Which is the smallest? Assume a range of [–420, 419] a = (0 | 1 | 3 | 2)RNS b = (0 | 1 | 4 | 1)RNS c = (0 | 6 | 2 | 1)RNS d = (2 | 0 | 0 | 2)RNS e = (5 | 0 | 1 | 0)RNS f = (7 | 6 | 4 | 2)RNS Answers: d < c < f < a < e < b –70 < –8 < –1 < 8 < 21 < 64

Approximate CRT Decoding: 

  Approximate CRT Decoding Theorem 4.1 (The Chinese remainder theorem, scaled version) Divide both sides of CRT equality by M to get scaled version of x in [0, 1) x = (xk–1 | . . . | x2 | x1 | x0)RNS =  i Mi ai ximi M x/M =  i ai ximi / mi 1 =  i gi(xi) 1 where mod-1 summation implies that we discard the integer parts Table 4.3 Values needed in applying the approximate Chinese remainder theorem decoding to RNS(8 | 7 | 5 | 3) –––––––––––––––––––––––––––––– i mi xi ai ximi / mi –––––––––––––––––––––––––––––– 3 8 0 .0000 1 .1250 2 .2500 3 .3750 . . . . . . Errors can be estimated and kept in check for the particular application

General RNS Division: 

  General RNS Division General RNS division, as opposed to division by one of the moduli (aka scaling), is difficult; hence, use of RNS is unlikely to be effective when an application requires many divisions Scheme proposed in 1994 PhD thesis of Ching-Yu Hung (UCSB): Use an algorithm that has built-in tolerance to imprecision, and apply the approximate CRT decoding to choose quotient digits Example –– SRT algorithm (s is the partial remainder) s < 0 quotient digit = –1 s  0 quotient digit = 0 s > 0 quotient digit = 1 The BSD quotient can be converted to RNS on the fly

4.5 Redundant RNS Representations: 

4.5 Redundant RNS Representations Fig. 4.3 Modulo-13 adder, with the output and one input being pseudoresidues in [0, 15]. Fig. 4.4 A modulo-m multiply-add cell that accumulates the sum into a double-length redundant pseudoresidue. [0, 15] [0, 12] [0, 15] [0, 11] if cout = 1 [0, 15]

4.6 Limits of Fast Arithmetic in RNS: 

4.6 Limits of Fast Arithmetic in RNS Known results from number theory Implications to speed of arithmetic in RNS Theorem 4.5: It is possible to represent all k-bit binary numbers in RNS with O(k / log k) moduli such that the largest modulus has O(log k) bits That is, with fast log-time adders, addition needs O(log log k) time Theorem 4.2: The ith prime pi is asymptotically i ln i Theorem 4.3: The number of primes in [1, n] is asymptotically n / ln n Theorem 4.4: The product of all primes in [1, n] is asymptotically en

Limits for Low-Cost RNS: 

Limits for Low-Cost RNS Known results from number theory Implications to speed of arithmetic in low-cost RNS Theorem 4.8: It is possible to represent all k-bit binary numbers in RNS with O((k / log k)1/2) low-cost moduli of the form 2a – 1 such that the largest modulus has O((k log k)1/2) bits Because a fast adder needs O(log k) time, asymptotically, low-cost RNS offers little speed advantage over standard binary Theorem 4.6: The numbers 2a – 1 and 2b – 1 are relatively prime iff a and b are relatively prime Theorem 4.7: The sum of the first i primes is asymptotically O(i2 ln i)

Disclaimer About RNS Representations: 

Disclaimer About RNS Representations RNS representations are sometimes referred to as “carry-free” However . . . even though each RNS digit is processed independently (for +, –, ), the size of the digit set is dependent on the desired range (grows at least double-logarithmically with the range M, or logarithmically with the word width k in the binary representation of the same range)

authorStream Live Help