1. Morgan Kaufmann Publishers Arithmetic for Computers
12 September, 2018
Arithmetic for Computers
Chapter 3 — Arithmetic for Computers

2. Morgan Kaufmann Publishers
12 September, 2018
Floating Point
Representation for non-integral numbers
Including very small and very large numbers
Like scientific notation
–2.34 × 1056
× 10–4
× 109
In binary
±1.xxxxxxx2 × 2yyyy
Types float and double in C
normalized
not normalized
Chapter 3 — Arithmetic for Computers

3. Floating Point Standard
Morgan Kaufmann Publishers
12 September, 2018
Floating Point Standard
Defined by IEEE Std
Developed in response to divergence of representations
Portability issues for scientific code
Now almost universally adopted
Two representations
Single precision (32-bit)
Double precision (64-bit)
Chapter 3 — Arithmetic for Computers

4. IEEE Floating-Point Format
Morgan Kaufmann Publishers
12 September, 2018
IEEE Floating-Point Format
single: 8 bits double: 11 bits
single: 23 bits double: 52 bits S
Exponent
Fraction
S: sign bit (0  non-negative, 1  negative)
Normalize significand: 1.0 ≤ |significand| < 2.0
Always has a leading pre-binary-point 1 bit, so no need to represent it explicitly (hidden bit)
Significand is Fraction with the “1.” restored
Exponent: excess representation: actual exponent + Bias
Ensures exponent is unsigned
Single: Bias = 127; Double: Bias = 1203
Chapter 3 — Arithmetic for Computers

5. Single-Precision Range
Morgan Kaufmann Publishers
12 September, 2018
Single-Precision Range
Exponents and reserved
Smallest value
Exponent:  actual exponent = 1 – 127 = –126
Fraction: 000…00  significand = 1.0
±1.0 × 2–126 ≈ ±1.2 × 10–38
Largest value
exponent:  actual exponent = 254 – 127 = +127
Fraction: 111…11  significand ≈ 2.0
±2.0 × ≈ ±3.4 × 10+38
Chapter 3 — Arithmetic for Computers

6. Double-Precision Range
Morgan Kaufmann Publishers
12 September, 2018
Double-Precision Range
Exponents 0000…00 and 1111…11 reserved
Smallest value
Exponent:  actual exponent = 1 – 1023 = –1022
Fraction: 000…00  significand = 1.0
±1.0 × 2–1022 ≈ ±2.2 × 10–308
Largest value
Exponent:  actual exponent = 2046 – 1023 = +1023
Fraction: 111…11  significand ≈ 2.0
±2.0 × ≈ ±1.8 ×
Chapter 3 — Arithmetic for Computers

7. Floating-Point Precision
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Precision
Relative precision
all fraction bits are significant
Single: approx 2–23
Equivalent to 23 × log102 ≈ 23 × 0.3 ≈ 6 decimal digits of precision
Double: approx 2–52
Equivalent to 52 × log102 ≈ 52 × 0.3 ≈ 16 decimal digits of precision
Chapter 3 — Arithmetic for Computers

8. Floating-Point Example
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Example
Represent –0.75
–0.75 = (–1)1 × 1.12 × 2–1
S = 1
Fraction = 1000…002
Exponent = –1 + Bias
Single: – = 126 =
Double: – = 1022 =
Single: …00
Double: …00
Chapter 3 — Arithmetic for Computers

9. Floating-Point Example
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Example
What number is represented by the single-precision float
…00
S = 1
Fraction = 01000…002
Exponent = = 129
x = (–1)1 × ( ) × 2(129 – 127)
= (–1) × 1.25 × 22
= –5.0
Chapter 3 — Arithmetic for Computers

10. Arithmetic for Computers
Morgan Kaufmann Publishers
12 September, 2018
Arithmetic for Computers
Operations on integers
Addition and subtraction
Multiplication and division
Dealing with overflow
Floating-point real numbers
Representation and operations
Chapter 3 — Arithmetic for Computers

11. Morgan Kaufmann Publishers
12 September, 2018
Integer Addition
Example: 7 + 6
Overflow if result out of range
Adding +ve and –ve operands, no overflow
Adding two +ve operands
Overflow if result sign is 1
Adding two –ve operands
Overflow if result sign is 0
Chapter 3 — Arithmetic for Computers

12. Morgan Kaufmann Publishers
12 September, 2018
Integer Subtraction
Add negation of second operand
Example: 7 – 6 = 7 + (–6)
+7: … –6: … : …
Overflow if result out of range
Subtracting two +ve or two –ve operands, no overflow
Subtracting +ve from –ve operand
Overflow if result sign is 0
Subtracting –ve from +ve operand
Overflow if result sign is 1
Chapter 3 — Arithmetic for Computers

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Dealing with Overflow
Some languages (e.g., C) ignore overflow
Use MIPS addu, addui, subu instructions
Other languages (e.g., Ada, Fortran) require raising an exception
Use MIPS add, addi, sub instructions
On overflow, invoke exception handler
Save PC in exception program counter (EPC) register
Jump to predefined handler address
mfc0 (move from coprocessor reg) instruction can retrieve EPC value, to return after corrective action
Chapter 3 — Arithmetic for Computers

14. Arithmetic for Multimedia
Morgan Kaufmann Publishers
12 September, 2018
Arithmetic for Multimedia
Graphics and media processing operates on vectors of 8-bit and 16-bit data
Use 64-bit adder, with partitioned carry chain
Operate on 8×8-bit, 4×16-bit, or 2×32-bit vectors
SIMD (single-instruction, multiple-data)
Saturating operations
On overflow, result is largest representable value
c.f. 2s-complement modulo arithmetic
E.g., clipping in audio, saturation in video
Chapter 3 — Arithmetic for Computers

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12 September, 2018
Multiplication
Start with long-multiplication approach
multiplicand
1000
× 1001
0000
multiplier
product
Length of product is the sum of operand lengths
Chapter 3 — Arithmetic for Computers

16. Multiplication Hardware
Morgan Kaufmann Publishers
Multiplication Hardware
12 September, 2018
Initially 0
Chapter 3 — Arithmetic for Computers

17. Multiplication Example (0010 * 0011)
Morgan Kaufmann Publishers
Multiplication Example (0010 * 0011)
12 September, 2018
Each step in an iteration handles one bit and takes 1 clock cycle
 each iteration = 3 clock cycles for 1 bit
32-bit multiplication = 32*3 ~ 100 clock cycles
+/- ops are 5 to 100 times more popular than *
Improvement: 1 clock cycle per iteration (bit)
Chapter 3 — Arithmetic for Computers

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12 September, 2018
Optimized Multiplier
Perform steps in parallel: add/shift, i.e.,
(shift mcand & mplier) in parallel with (mcand + mplier)
Multiplier placed in right half of Product register
Multiplicand in left half
One cycle per partial-product addition
That’s ok, if frequency of multiplications is low
Chapter 3 — Arithmetic for Computers

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Example
12 September, 2018
Product register at the beginning:
1001 (9)
× (5) Add mcand to left half? shift
(45)
Chapter 3 — Arithmetic for Computers

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12 September, 2018
Example: 2 * 3 = 0010 * 0011  Multiplicand = 0010 Xplier = 0011 Product register:  have to add mcand to left half of Prod register =  right shift entire Product register to get = (one (+ half) clock cycle) Next clock cycle to handle next bit:  add mcand to left half of Prod register to get  right shift entire Product register to get (one (+ half) clock cycle); since last two bits Since last two bits = 0: right shift two bits. Done.
Chapter 3 — Arithmetic for Computers

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Chapter 3 — Arithmetic for Computers

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12 September, 2018
Booth’s Algorithm
= 54
Beg. of run 1’s; End of run of 1’s
How do you know where the beg/end of a run is?
-- Look at the previous bit
Take 26 – 24 = 64 – 16 = 48
and 23 – 21 = 8 – 2 = 6
 = 54
Chapter 3 — Arithmetic for Computers

23. Morgan Kaufmann Publishers
Start from the right, for each multiplier bit, also examine bit to its right: 00: middle of a run of 0’s, do nothing 10: begininning of a run of 1’s, subtract multiplicand 01: end of a run of 1’s, add multiplicand 11: midlle of run of 1’s, do nothing 43 = (multiplicand) *12 = (multiplier) 0 = //multiplier bits 0_ (implicit at beginning) 0 = //multiplier bits 00 (shift 1, if needed) -172 = //multiplier bits 10 (shift 2) 0 = //multiplier bits 11 (shift 3, if needed) +688 = //multiplier bits 01 (shift 4) and add to sum 516 = = convert multiplicand to 2’s complement and shift bit corresponding to position of multiplier bit 2’s comp of 43 = Shifted left 2 bits = Add to current sum.
12 September, 2018
Chapter 3 — Arithmetic for Computers

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Faster Multiplier
12 September, 2018
Uses multiple adders
Cost/performance tradeoff
Can be pipelined
Several multiplication performed in parallel
Chapter 3 — Arithmetic for Computers

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12 September, 2018
MIPS Multiplication
Two 32-bit registers for product
HI: most-significant 32 bits
LO: least-significant 32-bits
Instructions
mult rs, rt / multu rs, rt
64-bit product in HI/LO
mfhi rd / mflo rd
Move from HI/LO to rd
Can test HI value to see if product overflows 32 bits
mul rd, rs, rt
Least-significant 32 bits of product –> rd
Chapter 3 — Arithmetic for Computers

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12 September, 2018
Division
Check for 0 divisor
Long division approach
If divisor ≤ dividend bits
1 bit in quotient, subtract
Otherwise
0 bit in quotient, bring down next dividend bit
Restoring division
Do the subtract, and if remainder goes < 0, add divisor back
Signed division
Divide using absolute values
Adjust sign of quotient and remainder as required
quotient
dividend
1001
-1000 10
101
1010
divisor
remainder
n-bit operands yield n-bit quotient and remainder
Chapter 3 — Arithmetic for Computers

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Division
12 September, 2018
Quotient: left shift & add 1 if reminder >=0
Divisor: in left half; shift right 1 bit after subtraction
Remainder: holds current dividend after subtract
Loop for all 32 bits:
{ Remainder -= Divisor
if Remainder < 0
Remainder += Divisor; # restore
Quotient << 1 and replace LSB = 0
else
Quotient << 1 and replace LSB = 1
Divisor >> 1; }
Quotient
Remainder
1001
100000
001010
-10000
-1000 10
Divisor
Remainder
Chapter 3 — Arithmetic for Computers

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Initially divisor in left half
Initially dividend
Chapter 3 — Arithmetic for Computers

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Optimized Divider
One cycle per partial-remainder subtraction
Looks a lot like a multiplier!
Same hardware can be used for both
Chapter 3 — Arithmetic for Computers

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Faster Division
Can’t use parallel hardware as in multiplier
Subtraction is conditional on sign of remainder
Faster dividers
e.g., Sweeney, Robertson, and Tocher (SRT) division determines quotient (lookup table) based on divisor and dividend.
Still require multiple steps
Chapter 3 — Arithmetic for Computers

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12 September, 2018
MIPS Division
Use HI/LO registers for result
HI: 32-bit remainder
LO: 32-bit quotient
Instructions
div rs, rt / divu rs, rt
No overflow or divide-by-0 checking
Software must perform checks if required
Use mfhi, mflo to access result
Chapter 3 — Arithmetic for Computers

32. Floating-Point Addition
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Addition
Consider a 4-digit decimal example
9.999 × × 10–1
1. Align decimal points
Shift number with smaller exponent
9.999 × × 101
2. Add significands
9.999 × × 101 = × 101
3. Normalize result & check for over/underflow
× 102
4. Round and renormalize if necessary
1.002 × 102
Chapter 3 — Arithmetic for Computers

33. Floating-Point Addition
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Addition
Now consider a 4-digit binary example
× 2–1 + – × 2–2 (0.5 + –0.4375)
1. Align binary points
Shift number with smaller exponent
× 2–1 + – × 2–1
2. Add significands
× 2–1 + – × 2–1 = × 2–1
3. Normalize result & check for over/underflow
× 2–4, with no over/underflow
4. Round and renormalize if necessary
× 2–4 (no change) =
Chapter 3 — Arithmetic for Computers

34. Morgan Kaufmann Publishers
12 September, 2018
FP Adder Hardware
Much more complex than integer adder
Doing it in one clock cycle would take too long
Much longer than integer operations
Slower clock would penalize all instructions
FP adder usually takes several cycles
Can be pipelined
Chapter 3 — Arithmetic for Computers

35. Floating-Point Multiplication
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Multiplication
Consider a 4-digit decimal example
1.110 × 1010 × × 10–5
1. Add exponents
For biased exponents, subtract bias from sum
New exponent = 10 + –5 = 5
2. Multiply significands
1.110 × =  × 105
3. Normalize result & check for over/underflow
× 106
4. Round and renormalize if necessary
1.021 × 106
5. Determine sign of result from signs of operands
× 106
Chapter 3 — Arithmetic for Computers

36. Floating-Point Multiplication
Morgan Kaufmann Publishers
12 September, 2018
Floating-Point Multiplication
Now consider a 4-digit binary example
× 2–1 × – × 2–2 (0.5 × –0.4375)
1. Add exponents
Unbiased: –1 + –2 = –3
Biased: (– ) + (– ) = – – 127 = –
2. Multiply significands
× =  × 2–3
3. Normalize result & check for over/underflow
× 2–3 (no change) with no over/underflow
4. Round and renormalize if necessary
× 2–3 (no change)
5. Determine sign: +ve × –ve  –ve
– × 2–3 = –
Chapter 3 — Arithmetic for Computers

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FP Adder Hardware
Step 1
Step 2
Step 3
Step 4
Chapter 3 — Arithmetic for Computers

38. FP Arithmetic Hardware
Morgan Kaufmann Publishers
12 September, 2018
FP Arithmetic Hardware
FP multiplier is of similar complexity to FP adder
But uses a multiplier for significands instead of an adder
FP arithmetic hardware usually does
Addition, subtraction, multiplication, division, reciprocal, square-root
FP  integer conversion
Operations usually takes several cycles
Can be pipelined
Chapter 3 — Arithmetic for Computers

39. Morgan Kaufmann Publishers
12 September, 2018
Accurate Arithmetic
IEEE Std 754 specifies additional rounding control
Extra bits of precision (guard, round, sticky)
Choice of rounding modes
Allows programmer to fine-tune numerical behavior of a computation
Not all FP units implement all options
Most programming languages and FP libraries just use defaults
Trade-off between hardware complexity, performance, and market requirements
Chapter 3 — Arithmetic for Computers