Quantization ideas

Low-precision training and inference formulations that keep accuracy within the benchmark's noise floor.

1 idea found

Category: Quantization ML: Attention Math: Numerical analysis Clear all

Unverified 2026

Power-of-Two Latency Attention

Make Laplacian latency attention hardware-friendly by quantizing the exponential decay and row normalization to powers of two. Exponentials and divisions can then be implemented with lookup tables and integer shifts, enabling a low-bit attention kernel for neuromorphic and edge accelerators.

Useful6/10

Difficulty4/10

Novelty7/10

Quantization Speedup Memory Scalability

Paper: Lapis: Laplacian Spiking Attention via First-Spike Timing and Membrane Leakage arXiv:2608.11865