TranSC: Hardware-Aware Design of Transcendental Functions Using Stochastic Logic

TL;DR

This paper presents TranSC, a stochastic computing-based method for efficient, accurate hardware implementation of transcendental functions, significantly reducing error and resource usage compared to existing solutions.

Contribution

It introduces a novel stochastic computing approach using quasi-random sequences for transcendental functions, improving accuracy and hardware efficiency.

Findings

Reduces MSE by up to 98%

Decreases hardware area by 33%

Lowers power consumption by 72%

Abstract

The hardware-friendly implementation of transcendental functions remains a longstanding challenge in design automation. These functions, which cannot be expressed as finite combinations of algebraic operations, pose significant complexity in digital circuit design. This study introduces a novel approach, TranSC, that utilizes stochastic computing (SC) for lightweight yet accurate implementation of transcendental functions. Building on established SC techniques, our method explores alternative random sources-specifically, quasi-random Van der Corput low-discrepancy (LD) sequences-instead of conventional pseudo-randomness. This shift enhances both the accuracy and efficiency of SC-based computations. We validate our approach through extensive experiments on various function types, including trigonometric, hyperbolic, and activation functions. The proposed design approach significantly reduces MSE by up to 98% compared to the state-of-the-art solutions while reducing hardware area, power consumption, and energy usage by 33%, 72%, and 64%, respectively.