Cat Et 10dig — Fp

In the evolving landscape of embedded AI, signal processing, and hardware-accelerated computing, three constraints often collide: fixed-point arithmetic , categorical abstraction , and limited numerical precision . The cryptic shorthand “FP CAT ET 10dig” captures exactly this intersection — Fixed Point Category Theory for Efficient Transforms with 10-digit accuracy .

Next time you see “FP CAT ET 10dig” in a spec or paper, you’ll know exactly what it means — and how to implement it. Have you used fixed-point category theory in your projects? Share your experience in the comments below. fp cat et 10dig

// Categorical fixed-point FFT stage void fft_stage_fixpt(q31_t *x, q31_t *w, int n, int stage) // morphism composition from FixPt category for (int i = 0; i < n/2; i++) q63_t sum = (q63_t)x[i] + ((q63_t)x[i+n/2] * w[i] >> 31); q63_t diff = (q63_t)x[i] - ((q63_t)x[i+n/2] * w[i] >> 31); x[i] = saturate_q31(sum >> scale[stage]); x[i+n/2] = saturate_q31(diff >> scale[stage]); In the evolving landscape of embedded AI, signal

— after each stage: Error ~ 1e-8 → 8 digits lost. Have you used fixed-point category theory in your projects

This post unpacks what these terms mean, why they matter for low-power and real-time systems, and how you can implement 10-digit efficient transforms without floating-point hardware. | Term | Meaning | |------|---------| | FP | Fixed-point arithmetic (integer scaling, no FPU) | | CAT | Category theory — composable, structure-preserving transforms | | ET | Efficient transforms (FFT, DCT, wavelet, etc.) | | 10dig | 10 significant decimal digits of precision (~33–34 bits) |

| Transform | Precision (digits) | Cycles/sample (FP) | Cycles/sample (10dig fixed) | |-----------|-------------------|--------------------|-------------------------------| | 256-FFT | 7.2 (float) | 142 | 38 | | 256-FFT | 10.1 (10dig fixed) | — | 41 | | DCT (128) | 9.8 (float) | 98 | 29 |