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This example shows how to perform basic fixed-point arithmetic operations.

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Further Considerations for Addition and Subtraction Math With Other Built-In Data Types |

Whenever you add two unsigned fixed-point numbers, you may need a carry bit to correctly represent the result. For this reason, when adding two B-bit numbers (with the same scaling), the resulting value has an extra bit compared to the two operands used.

a = ufi(0.234375,4,6); c = a + a

c = 0.4688 DataTypeMode: Fixed-point: binary point scaling Signedness: Unsigned WordLength: 5 FractionLength: 6

a.bin

ans = 1111

c.bin

ans = 11110

With signed, two's-complement numbers, a similar scenario occurs because of the sign extension required to correctly represent the result.

a = sfi(0.078125,4,6); b = sfi(-0.125,4,6); c = a + b

c = -0.0469 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 5 FractionLength: 6

a.bin

ans = 0101

b.bin

ans = 1000

c.bin

ans = 11101

If you add or subtract two numbers with different precision, the radix point first needs to be aligned to perform the operation. The result is that there is a difference of more than one bit between the result of the operation and the operands (depending on how far apart the radix points are).

a = sfi(pi,16,13); b = sfi(0.1,12,14); c = a + b

c = 3.2416 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 18 FractionLength: 14

**Further Considerations for Addition and Subtraction**

Note that the following pattern is **not** recommended. Since scalar additions are performed at each iteration in the for-loop, a bit is added to temp during each iteration. As a result, instead of a ceil(log2(Nadds)) bit-growth, the bit-growth is equal to Nadds.

s = rng; rng('default'); b = sfi(4*rand(16,1)-2,32,30); rng(s); % restore RNG state Nadds = length(b) - 1; temp = b(1); for n = 1:Nadds temp = temp + b(n+1); % temp has 15 more bits than b end

If the `sum` command is used instead, the bit-growth is curbed as expected.

```
c = sum(b) % c has 4 more bits than b
```

c = 7.0059 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 36 FractionLength: 30

In general, a full precision product requires a word length equal to the sum of the word lengths of the operands. In the following example, note that the word length of the product `c` is equal to the word length of `a` plus the word length of `b`. The fraction length of `c` is also equal to the fraction length of `a` plus the fraction length of `b`.

a = sfi(pi,20); b = sfi(exp(1),16); c = a * b

c = 8.5397 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 36 FractionLength: 30

When you assign a fixed-point value into a pre-defined variable, quantization might be involved. In such cases, the right-hand-side of the expression is quantized by rounding to nearest and then saturating, if necessary, before assigning to the left-hand-side.

N = 10; a = sfi(2*rand(N,1)-1,16,15); b = sfi(2*rand(N,1)-1,16,15); c = sfi(zeros(N,1),16,14); for n = 1:N c(n) = a(n).*b(n); end

Note that when the product `a(n).*b(n)` is computed with full precision, an intermediate result with wordlength 32 and fraction length 30 is generated. That result is then quantized to a wordlength of 16 and a fraction length of 14, as explained above. The quantized value is then assigned to the element `c(n)`.

Often, it is not desirable to round to nearest or to saturate when quantizing a result because of the extra logic/computation required. It also may be undesirable to have to assign to a left-hand-side value to perform the quantization. You can use `QUANTIZE` for such purposes. A common case is a feedback-loop. If no quantization is introduced, un-bounded bit-growth will occur as more input data is provided.

a = sfi(0.1,16,18); x = sfi(2*rand(128,1)-1,16,15); y = sfi(zeros(size(x)),16,14); for n = 1:length(x) z = y(n); y(n) = x(n) - quantize(a.*z, true, 16, 14, 'Floor', 'Wrap'); end

In this example, the product `a.*z` is computed with full precision and is subsequently quantized to a wordlength of 16 bits and a fraction length of 14. The quantization is done by rounding to floor (truncation) and allowing for wrapping if overflow occurs. Quantization still occurs at assignment, because the expression `x(n) - quantize(a.*z, ...)` produces an intermediate result of 18 bits and y is defined to have 16 bits. To eliminate the quantization at assignment, you can introduce an additional explicit quantization as shown below. The advantage of doing this is that no round-to-nearest/saturation logic is used. The left-hand-side result has the same 16-bit wordlength and fraction length of 14 as `y(n)`, so no quantization is necessary.

a = sfi(0.1,16,18); x = sfi(2*rand(128,1)-1,16,15); y = sfi(zeros(size(x)),16,14); T = numerictype(true, 16, 14); for n = 1:length(x) z = y(n); y(n) = quantize(x(n), T, 'Floor', 'Wrap') - ... quantize(a.*z, T, 'Floor', 'Wrap'); end

Full-precision sums are not always desirable. For example, the 18-bit wordlength corresponding to the intermediate result `x(n) - quantize(...)` above may result in complicated and inefficient code, if C code is generated. Instead, it may be desirable to keep all results of addition/subtraction to 16 bits. You can use the `accumpos` and `accumneg` functions for this purpose.

a = sfi(0.1,16,18); x = sfi(2*rand(128,1)-1,16,15); y = sfi(zeros(size(x)),16,14); T = numerictype(true, 16, 14); for n = 1:length(x) z = y(n); y(n) = quantize(x(n), T); % defaults: 'Floor','Wrap' y(n) = accumneg(y(n), quantize(a.*z, T)); % defaults: 'Floor','Wrap' end

`accumpos` and `accumneg` are well-suited to model accumulators. The behavior corresponds to the += and -= operators in C. A common example is an FIR filter in which the coefficients and input data are represented with 16 bits. The multiplication is performed in full-precision, yielding 32 bits, and an accumulator with 8 guard-bits, i.e. 40-bits total is used to enable up to 256 accumulations without the possibility of overflow.

b = sfi(1/256*[1:128,128:-1:1],16); % Filter coefficients x = sfi(2*rand(300,1)-1,16,15); % Input data z = sfi(zeros(256,1),16,15); % Used to store the states y = sfi(zeros(size(x)),40,31); % Initialize Output data for n = 1:length(x) acc = sfi(0,40,31); % Reset accumulator z(1) = x(n); % Load input sample for k = 1:length(b) acc = accumpos(acc,b(k).*z(k)); % Multiply and accumulate end z(2:end) = z(1:end-1); % Update states y(n) = acc; % Assign output end

To simplify syntax and shorten simulation time, you can use matrix arithmetic. For the FIR filter example, you can replace the inner loop with an inner product.

z = sfi(zeros(256,1),16,15); % Used to store the states y = sfi(zeros(size(x)),40,31); for n = 1:length(x) z(1) = x(n); y(n) = b*z; z(2:end) = z(1:end-1); end

The inner product `b*z` is performed with full precision. Because this is a matrix operation, the bit growth is due to both the multiplication involved and the addition of the resulting products. Therefore, the bit growth depends on the length of the operands. Since `b` and `z` have length 256, that accounts for an 8-bit growth due to the additions. This is why the inner product results in 32 + 8 = 40 bits (with fraction length 31). Since this is the format `y` is initialized to, no quantization occurs in the assignment `y(n) = b*z`.

If you had to perform an inner product for more than 256 coefficients, the bit growth would be more than 8 bits beyond the 32 needed for the product. If you only had a 40-bit accumulator, you could model the behavior by either introducing a quantizer, as in `y(n) = quantize(Q,b*z)`, or you could use the `accumpos` function as has been shown.

`accumpos` can be used to model a simple counter which naturally wraps after reaching its maximum value. For example, you can model a 3-bit counter as follows.

c = ufi(0,3,0); Ncounts = 20; % Number of times to count for n = 1:Ncounts c = accumpos(c,1); end

Since the 3-bit counter naturally wraps back to 0 after reaching 7, the final value of the counter is mod(20,8) = 4.

**Math With Other Built-In Data Types**

When doing arithmetic between `fi` and `double`, the double is cast to a `fi` with the same word length and signedness of the `fi`, and best-precision fraction length. The result of the operation is a `fi`.

a = fi(pi); b = 0.5 * a

b = 1.5708 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 32 FractionLength: 28

**Some Differences Between MATLAB® and C**

Note that in C, the result of an operation between an integer data type and a double data type promotes to a double.

However, in MATLAB, the result of an operation between a built-in integer data type and a double data type is an integer. In this respect, the `fi` object behaves like the built-in integer data types in MATLAB. The result of an operation between a `fi` and a double is a `fi`.

When doing arithmetic between fi and one of the built-in integer data types [u]int[8,16,32], the word length and signedness of the integer are preserved. The result of the operation is a fi.

a = fi(pi); b = int8(2) * a

b = 6.2832 DataTypeMode: Fixed-point: binary point scaling Signedness: Signed WordLength: 24 FractionLength: 13

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