I am struggling with how to implement arithmetic on fixed-point numbers of different precision. I have read the paper by R. Yates, but I'm still lost. In what follows, I use Yates's notation, in which A(n,m)
designates a signed fixed-point format with n
integer bits, m
fraction bits, and n + m + 1
bits overall.
Short question: How exactly is a A(a,b)*A(c,d)
and A(a,b)+A(c,d)
carried out when a
!= c
and b
!= d
?
Long question: In my FFT algorithm, I am generating a random signal having values between -10V and 10V signed input(in) which is scaled to A(15,16)
, and the twiddle factors (tw) are scaled to A(2,29)
. Both are stored as int
s. Something like this:
float temp = (((float)rand() / (float)(RAND_MAX)) * (MAX_SIG - MIN_SIG)) + MIN_SIG;
int in_seq[i][j] = (int)(roundf(temp *(1 << numFracBits)));
And similarly for the twiddle factors.
Now I need to perform
res = a*tw
Questions:
a) how do I implement this?
b) Should the size of res
be 64 bit?
c) can I make 'res' A(17,14) since I know the ranges of a
and tw
? if yes, should I be scaling a*tw
by 2^14 to store correct value in res
?
a + res
Questions:
a) How do I add these two numbers of different Q formats?
b) if not, how do I do this operation?
In computing, a fixed-point number representation is a real data type for a number that has a fixed number of digits after (and sometimes also before) the radix point (after the decimal point '.' in English decimal notation).
Therefore, the benefit of fixed point arithmetic is that they are as straight-forward and efficient as integers arithmetic in computers. We can reuse all the hardware built to for integer arithmetic to perform real numbers arithmetic using fixed point representation.
Recall all arithmetics on fixed point numbers are the same as integer, we can simply reuse the integer type intin C to perform fixed point arithmetic. The position of binary point only matters in cases when we print it on screen or perform arithmetic with different "type" (such as when adding intto fixed<32,6>).
Fixed-point representation allows us to use fractional numbers on low-cost integer hardware. To lower the cost of the implementation, many digital signal processors are designed to perform arithmetic operations only on integer numbers. To represent fractional numbers on these processors, we can use an implied binary point.
Maybe it's easiest to make an example.
Suppose you want to add two numbers, one in the format A(3, 5)
, and the other in the format A(2, 10)
.
You can do it by converting both numbers to a "common" format - that is, they should have the same number of bits in the fractional part.
A conservative way of doing that is to choose the greater number of bits. That is, convert the first number to A(3, 10)
by shifting it 5 bits left. Then, add the second number.
The result of an addition has the range of the greater format, plus 1 bit. In my example, if you add A(3, 10)
and A(2, 10)
, the result has the format A(4, 10)
.
I call this the "conservative" way because you cannot lose information - it guarantees that the result is representable in the fixed-point format, without losing precision. However, in practice, you will want to use smaller formats for your calculation results. To do that, consider these ideas:
A(2, 5)
by shifting the integer right by 5 bits. This will lose precision, and usually this precision loss is not problematic, because you are going to add a less-precise number to it anyway.Now, on multiplication.
It's possible to multiply two fixed-point numbers directly - they can be in any format. The format of the result is the "sum of the input formats" - all the parts added together - and add 1 to the integer part. In my example, multiplying A(3, 5)
with A(2, 10)
gives a number in the format A(7, 15)
. This is a conservative rule - the output format is able to store the result without loss of precision, but in applications, almost always you want to cut the precision of the output, because it's just too many bits.
In your case, where the number of bits for all numbers is 32, you probably want to lose precision in such a way that all intermediate results have 32 bits.
For example, multiplying A(17, 14)
with A(2, 29)
gives A(20, 43)
- 64 bits required. You probably should cut 32 bits from it, and throw away the rest. What is the range of the result? If your twiddle factor is a number up to 4, the result is probably limited by 2^19 (the conservative number 20 above is needed to accommodate the edge case of multiplying -1 << 31
by -1 << 31
- it's almost always worth rejecting this edge-case).
So use A(19, 12)
for your output format, i.e. remove 31 bits from the fractional part of your output.
So, instead of
res = a*tw;
you probably want
int64_t res_tmp = (int64_t)a * tw; // A(20, 43)
if (res_tmp == ((int64_t)1 << 62)) // you might want to neglect this edge case
--res_tmp; // A(19, 43)
int32_t res = (int32_t)(res_tmp >> 31); // A(19, 12)
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