fi Best Practices Review

Reviews MATLAB code for fixed-point (fi) best practices and suggests improvements for performance, code generation efficiency, and correctness.

When to Use

• Reviewing MATLAB code that uses fi, fimath, numerictype, or quantizenumeric
• Optimizing fixed-point simulation speed
• Preparing fixed-point code for C or hardware code generation

When Not to Use

• Code using only built-in integer types (int8, uint16, etc.) without fi
• Pure floating-point algorithms with no fixed-point intent
• Simulink-only workflows where fixed-point is configured through block dialogs (use Fixed-Point Tool instead)

Checklist

When reviewing code, check for ALL of the following:

1. Vectorize fi() Calls

Problem: Scalar fi() in a loop is slow due to per-element object construction overhead.

Fix: Pass entire arrays to fi() at once.

% BAD, slow: per-element fi object construction
for k = 1:N
    x_fi(k) = fi(x(k), 1, 18, 16, F);
end

% GOOD, fast: single vectorized call, bit-true identical result
x_fi = fi(x, 1, 18, 16, F);

2. Separate Data Types from Algorithm

Problem: Hardcoding fi types inside algorithm code makes it impossible to switch between float/fixed or compare configurations.

Fix: Use a types table with empty prototypes and cast(...,'like',...).

% Types table (separate function)
function T = mytypes(dt)
  switch dt
    case 'double'
      T.b = double([]);  T.x = double([]);  T.y = double([]);
    case 'single'
      T.b = single([]);  T.x = single([]);  T.y = single([]);
    case 'fixed16'
      F = fimath('RoundingMethod','Floor','OverflowAction','Wrap', ...
                 'ProductMode','KeepLSB','ProductWordLength',32, ...
                 'SumMode','KeepLSB','SumWordLength',32);
      T.b = fi([], 1, 16, 15, F);
      T.x = fi([], 1, 16, 15, F);
      T.y = fi([], 1, 16, 14, F);
  end
end