1. ECE/CS 552: Floating Point
© Prof. Mikko Lipasti
Lecture notes based in part on slides created by Mark Hill, David Wood, Guri Sohi, John Shen and Jim Smith

2. Basic Arithmetic and the ALU
Now
Floating point representation
Floating point addition, multiplication
These are not crucial for the project

3. Floating Point Want to represent larger range of numbers
Fixed point (integer): -2n-1 … (2n-1 –1)
How? Sacrifice precision for range by providing exponent to shift relative weight of each bit position
Similar to scientific notation:
x 1023
Cannot specify every discrete value in the range, but can span much larger range

4. Floating Point Still use a fixed number of bits IEEE 754 standard
Sign bit S, exponent E, significand F
Value: (-1)S x F x 2E
IEEE 754 standard S E F
Size
Exponent
Significand
Range
Single precision
32b 8b
23b
2x10+/-38
Double precision
64b
11b
52b
2x10+/-308

5. Floating Point Exponent
Exponent specified in biased or excess notation
Why?
To simplify sorting
Sign bit is MSB to ease sorting
2’s complement exponent:
Large numbers have positive exponent
Small numbers have negative exponent
Sorting does not follow naturally

6. Excess or Biased Exponent
2’s Compl
Excess-127
-127
-126 …
+127
Value: (-1)S x F x 2(E-bias)
SP: bias is 127
DP: bias is 1023

7. Floating Point Normalization
S,E,F representation allows more than one representation for a particular value, e.g.
1.0 x 105 = 0.1 x 106 = x 104
This makes comparison operations difficult
Prefer to have a single representation
Hence, normalize by convention:
Only one digit to the left of the floating point
In binary, that digit must be a 1
Since leading ‘1’ is implicit, no need to store it
Hence, obtain one extra bit of precision for free

8. FP Overflow/Underflow
Analogous to integer overflow
Result is too big to represent
Means exponent is too big
FP Underflow
Result is too small to represent
Means exponent is too small (too negative)
Both can raise an exception under IEEE754

9. IEEE754 Special Cases Single Precision Double Precision Value Exponent
Significand
nonzero
denormalized
1-254
anything
1-2046
fp number
255
2047
infinity
NaN (Not a Number)

10. FP Rounding Rounding is important FP rounding hardware helps
Small errors accumulate over billions of ops
FP rounding hardware helps
Compute extra guard bit beyond 23/52 bits
Further, compute additional round bit beyond that
Multiply may result in leading 0 bit, normalize shifts guard bit into product, leaving round bit for rounding
Finally, keep sticky bit that is set whenever ‘1’ bits are “lost” to the right
Differentiates between 0.5 and

11. Floating Point Addition
Just like grade school
First, align decimal points
Then, add significands
Finally, normalize result
Example
9.997 x 102
x 102
4.631 x 10-1
x 102
Sum
x 102
Normalized
x 103

12. FP Adder

13. FP Multiplication Sign: Ps = As xor Bs Exponent: PE = AE + BE
Due to bias/excess, must subtract bias
e = e1 + e2
E = e = e1 + e
E = (E1 – 1023) + (E2 – 1023)
E = E1 + E2 –1023
Significand: PF = AF x BF
Standard integer multiply (23b or 52b + g/r/s bits)
Use Wallace tree of CSAs to sum partial products

14. FP Multiplication Compute sign, exponent, significand Normalize
Shift left, right by 1
Check for overflow, underflow
Round
Normalize again (if necessary)

15. Summary Floating point representation Floating point add
Normalization
Overflow, underflow
Rounding
Floating point add
Floating point multiply