用随机计算实现超越函数，精度效率双提升。
TranSC: Hardware-Aware Design of Transcendental Functions Using Stochastic Logic
- 改用范德科普低差异序列替代伪随机数，提升计算精度
- 相比现有方法，均方误差降低98%，硬件开销减少33%
- 适合需要低功耗高精度的嵌入式系统设计
超越函数在数字电路设计中因无法用有限代数运算表达，始终面临硬件友好实现的挑战。本文提出一种新方法 TranSC，利用随机计算（SC）实现轻量且高精度的超越函数硬件部署。该方法创新性地采用准随机范德科普低差异（LD）序列替代传统伪随机数，显著提升基于随机计算的运算精度与效率。通过在多种函数类型（包括三角函数、双曲函数及激活函数）上的大量实验验证，所提方案相较当前最优方法，均方误差（MSE）最高降低98%，同时硬件面积、功耗和能量分别减少33%、72%和64%。
原文摘要 · Abstract (English)
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.
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