Use exact math semantics

When implementing performance-critical algorithms (e.g., pooling, math kernels, SIMD/ISA code), preserve the algorithm’s true numeric semantics and parameterization:

1) Match rounding/truncation semantics with the ISA’s exact instruction

• Prefer the native intrinsic that implements the intended operation (e.g., truncation toward zero) rather than approximating via integer conversion that may differ.
• Example (aarch64):

  static inline float32x4_t trunc_ps(const float32x4_t& x)
  {
  #if __aarch64__
    return vrndq_f32(x); // trunc toward zero
  #else
    // fallback, but verify it matches trunc-toward-zero semantics
    int32x4_t xi = vcvtq_s32_f32(x);
    return vcvtq_f32_s32(xi);
  #endif
  }
  

2) Don’t “constant-fold” parameters that are mathematically location-dependent

• If kernel extents (or other algorithm parameters) vary per output position, compute them per position in the same execution space (shader/CPU) or pass the per-position values explicitly.
• For adaptive pooling: kernel_w/kernel_h generally depend on gx/gy (output location), so computing a single constant kernel on CPU and reusing it is incorrect. Keep the dynamic computation consistent with the reference definitions.

3) Add a quick correctness checklist during review

• Numeric ops: confirm rounding direction (floor/ceil/trunc) and tie behavior where relevant.
• Parameter definition: confirm which variables are constant vs per-location/per-channel.
• Kernel wiring: verify parameter order (e.g., bias vs kernel) to avoid silent swaps that still compile.

Applying this standard prevents cross-architecture drift (wrong rounding) and algorithm drift (wrong kernel sizes), which are frequent root causes of subtle accuracy regressions in algorithmic code paths.