Hardware-aware Pseudo-Softmax: a research note

This note organizes a hardware-aware view of approximating Softmax: the target is not only model accuracy, but an implementation that can be measured, verified, and mapped to efficient hardware.

The relevant questions are about approximation error, memory, parallelism, RTL constraints, and the impact of replacing an expensive operation inside a larger model.

A research note for organizing the intuition behind approximating Softmax when the goal is efficient and verifiable hardware.

Hardware-aware-ai

Hardware-aware Pseudo-Softmax: a research note