Presentation is loading. Please wait.

Presentation is loading. Please wait.

Chapter 13 Numerical Issues. Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 2 Learning Objectives  Numerical issues.

Similar presentations


Presentation on theme: "Chapter 13 Numerical Issues. Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 2 Learning Objectives  Numerical issues."— Presentation transcript:

1 Chapter 13 Numerical Issues

2 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 2 Learning Objectives  Numerical issues and data formats.  Fixed point.  Fractional number.  Floating point.  Comparison of formats and dynamic ranges.

3 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 3 Numerical Issues and Data Formats C6000 Numerical Representation Fixed point arithmetic:  16-bit (integer or fractional).  Signed or unsigned. Floating point arithmetic:  32-bit single precision.  64-bit double precision.

4 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 4 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Unsigned integer numbers

5 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 5 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Unsigned integer numbers

6 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 6 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Unsigned integer numbers

7 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 7 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Unsigned integer numbers

8 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 8 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

9 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 9 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

10 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 10 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

11 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 11 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

12 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 12 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

13 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 13 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

14 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 14 Fixed Point Arithmetic - Definition  For simplicity a 4-bit representation is used: Decimal Equivalent Binary Number Signed integer numbers

15 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 15 Fixed Point Arithmetic - Problems  The following equation is the basis of many DSP algorithms (See Chapter 1):  Two problems arise when using signed and unsigned integers:  Multiplication overflow.  Addition overflow.

16 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 16  16-bit x 16-bit = 32-bit  Example: using 4-bit representation  24 cannot be represented with 4-bits. Multiplication Overflow x x

17 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 17  32-bit + 32-bit = 33-bit  Example: using 4-bit representation  16 cannot be represented with 4-bits. Addition Overflow

18 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 18 Fixed Point Arithmetic - Solution  The solutions for reducing the overflow problem are:  Saturate the result.  Use double precision result.  Use fractional arithmetic.  Use floating point arithmetic.

19 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 19 Solution - Saturate the result  Unsigned numbers:  If A x B  15  result = A x B  If A x B > 15  result = x Saturated

20 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 20 Solution - Saturate the result  Signed numbers:  If -8  A x B  7  result = A x B  If A x B > 7  result = 7  If A x B < -8  result = x Saturated

21 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 21 Solution - Double precision result  For a 4-bit x 4-bit multiplication hold the result in an 8-bit location.  Problems:  Uses more memory for storing data.  If the result is used in another multiplication the data needs to be represented into single precision format (e.g. prod = prod x sum).  Results need to be scaled down if it is to be sent to an A/D converter.

22 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 22 Solution - Fractional arithmetic  If A and B are fractional then:  A x B < min(A, B)  i.e. The result is less than the operands hence it will never overflow.  Examples:  0.6 x 0.2 = 0.12 (0.12 < 0.6 and 0.12 < 0.2)  0.9 x 0.9 = 0.81 (0.81 < 0.9)  0.1 x 0.1 = 0.01 (0.01 < 0.1)

23 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide (N-1) + Fractional numbers  Definition: (N-1) 0111 = MAX 0001 = 2 -(N-1) 1000 = MAX+2 -(N-1) = 1 MAX = 1-2 -(N-1)  Largest Number:  What is the largest number?

24 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 24 Fractional numbers  Definition: (N-1) (N-1) = MIN = -1  For 16-bit representation:  MAX = =  MIN = -1  -1  x < 1  Smallest Number:  What is the smallest number?

25 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 25 Fractional numbers - Sign Extension  To keep the same resolution as the operands we need to select these 4-bits: 0110a= = = b= = = Sign extension 1110 x

26 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 26 Fractional numbers - Sign Extension  The way to do it is to shift left by one bit and store upper 4-bits or right shift by three and store the lower 4-bits: 0110 a= = = b= = = Sign extension 1110 x Sign extension bits

27 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 27CPU MPY A3,A4,A6 NOPQ15 s.s.xxxxxxxxxxxxxxx s.s.yyyyyyyyyyyyyyy x Q15 s.s.szzzzzzzzzzzzzzzzzzzzzzzzzzzzzz Q30 15-bit * 15-bit Multiplication Store to Data Memory SHR A6,15,A6 SHR A6,15,A6 STH A6,*A7 STH A6,*A7 s.s.zzzzzzzzzzzzzzz Q15

28 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 28 ‘C6000 C Data Types TypeSizeRepresentation char, signed char8 bitsASCII unsigned char8 bitsASCII short16 bits2’s complement unsigned short16 bitsbinary int, signed int32 bits2s complement unsigned int32 bitsbinary long, signed long40 bits 2’s complement unsigned long40 bits binary enum32 bits 2’s complement float32 bits IEEE 32-bit double64 bits IEEE 64-bit long double64 bits IEEE 64-bit pointers32 bits binary

29 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 29  Pseudo assembly language:  Pseudo ‘C’ language: Fractional numbers - Sign Extension A0 = 0x ; initial value A1 = 0.5; initial value A2 = 0.5; initial value A3 = 0; initial value MPY A1, A2, A3; A3 = 0x SHL A3,1,A3; A3 = 0x STH A3, *A0; 0x2000 -> 0x or MPY A1, A2, A3; A3 = 0x SHR A3,15,A3; A3 = 0x STH A3, *A0; 0x2000 -> 0x short a, b, result; int prod; prod = a * b; prod = prod >> 15; result = (short) prod;

30 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 30 Fractional numbers - Problems  There are some problems that need to be resolved when using fractional numbers.  These are:  Result of -1 x -1 = 1  Accumulative overflow.

31 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 31 Problem of -1 x -1  We have seen that:  -1  x < 1  -1 x -1 = 1 which cannot be represented.  Solution:  There are two instructions that saturate the result if you have -1 x -1: SMPYSMPYH

32 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 32 Problem of -1 x -1  In one cycle these instructions do the following:  Multiply.  Shift left by 1-bit.  Saturate if the sign bits are 01.  It can be shown that: Positive Result Negative Result -1 x -1 Result Result of MPY(H) 00.xxx-xb11.xxx-xb01.xxx-xb Result of SMPY(H) 0.xxx-xb1.xxx-xb0.xxx-xb

33 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 33 Problem of Accumulative Overflow  In this case the overflow is due to the summation.  Examples of overflow: 0x7fff + 0x0002 = 0x8001 0x7fff + 0x0002 = 0x8001 0x7ffe0x00000xffff0x7fff 0x8001 (positive number + positive number = negative number!) 0xffff + 0x0002 = 0x0001 0xffff + 0x0002 = 0x0001 (negative number + positive number = negative number!)

34 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 34 Problem of Accumulative Overflow  Solutions: (1)Saturate the intermediate results by using these add instructions: If saturation occurs the SAT bit in the CSR is set to 1. You must clear it. (2)Use guard bits: e.g. ADD A1, A2, A1:A0 SADDSSUB

35 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 35 Problem of Accumulative Overflow  Solutions: (3)Do nothing if the system is Non-Gain: With a non-gain system the final result is always less than unity. Example system: This will be non-gain if:

36 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 36 Floating Point Arithmetic  The C67xx support both single and double precision floating point formats.  The single precision format is as follows: s 31 e 30 e 2221 eem... m0mm... 1-bit8-bits23-bits value = (-1) sign * (1.mantissa) * 2 (exponent-127) s = sign bit e = exponent (8-bit biased : -127) m = mantissa (23-bit normalised fraction)

37 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 37 Floating Point Arithmetic Example  Example: Conversion between integer and floating point.  Convert ‘dd’ to the IEEE floating point format: int dd = 0x ; flot1 = (float) dd;

38 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 38 Floating Point Arithmetic Example flot1 = 0x4EC To view the value of “flot1” use: View: Memory:Address= &flot1 We find:

39 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 39 Floating Point Arithmetic Example  Let us check to see if we have the same number: s = 0 e = b = = 157 m = 0.100b = 0.5 float1 = (-1) 0 * (1.5) * 2 ( ) = 1.5 * 2 30 = decimal = 0x

40 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 40 Floating Point Arithmetic Example  The previous example can be seen in:  numerical.pjt  Numerical_.ws  Use the mixed mode display to see the assembly code.

41 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 41 Floating Point IEEE Standard  Special values: s 01ss01s e 0000

42 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 42 Floating Point IEEE Standard  Dynamic range:  Largest positive number:  e(max) = 255,  m(max) = 1-2 -(23-1)  max = [1 + ( )] * = 3.4 *  Smallest positive number:  e(min) = 0,  m(min) = 0.5 (normalised 0.100…0b)  min= 1.5 * = * value = (-1) sign * (1.mantissa) * 2 (exponent-127)

43 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 43 Floating Point IEEE Standard  Dynamic range:  Largest negative number:  e(max) = 255,  m(max) =  max = [-1 + ( )] * = -3.4 *  Smallest negative number:  e(min) = 0,  m(min) = 0.5 (normalised 1.100…0b)  min= -1.5 * = * value = (-1) sign * (1.mantissa) * 2 (exponent-127)

44 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 44 Floating/Fixed Point Summary  Floating point single precision:  Floating point double precision: s 31 e 30 e 2322 eem... m0mm... 1-bit8-bits23-bits s 63 e 62 e 5251 eem... m 0 mm... 1-bit11-bits52-bits value = (-1) s * 1.m * 2 e-127 value = (-1) s * 1.m * 2 e-1023 odd:even registers

45 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 45 Floating/Fixed Point Summary (Short: N = 16;Int: N = 32)  Unsigned integer:  Signed integer:  Signed fractional: x 2 N xx x -2 N xx x (N-1) x... x 2 -1 x 2 -2

46 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 46 Floating/Fixed Point Dynamic Range Smallest Number (positive) Largest Number (positive) Smallest Number (negative) Floating Point Single Precision 3.4 x x x bit bit bit bit Integer Fixed Point Fractional

47 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 47 Numerical Issues - Useful Tips  Multiply by 2: Use shift left  Divide by 2:Use shift right  Log 2 N:Use shift  Sine, Cosine, Log:Use look up tables  To convert a fractional number to hex:  Num x 2 15  Then convert to hex e.g: convert 0.5 to hex  0.5 x 2 15 =  (16384) dec = (0x4000) hex

48 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 48 Numerical Issues - 32-bit Multiplication  It is possible to perform 32-bit multiplication using 16-bit multipliers.  Example: c = a x b (with 32-bit values). ahahahah alalalal bhbhbhbh blblblbl a = b = 32-bits a * b =(a h << 16 + a l )* (b h << 16 + b l ) a * b =(a h << 16 + a l )* (b h << 16 + b l ) =[(a h * b h ) << 32] + [(a l * b h ) << 16] + [(a h * b l ) << 16] + [a l * b l ]

49 Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 49Links  Further reading:  Understanding TMS320C62xx DSP Single-precision Floating-Point Functions: \Links\spra515.pdf \Links\spra515.pdf  TMS320C6000 Integer Division: \Links\spra707.pdf \Links\spra707.pdf

50 Chapter 13 Numerical Issues - End -


Download ppt "Chapter 13 Numerical Issues. Dr. Naim Dahnoun, Bristol University, (c) Texas Instruments 2002 Chapter 13, Slide 2 Learning Objectives  Numerical issues."

Similar presentations


Ads by Google