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.

翻译：超越函数的硬件友好实现一直是设计自动化领域长期存在的挑战。这些无法表示为代数运算有限组合的函数，在数字电路设计中带来了显著的复杂性。本研究提出了一种新颖方法TranSC，利用随机计算（SC）实现轻量级且精确的超越函数。基于成熟的SC技术，我们的方法探索了替代随机源——特别是准随机Van der Corput低差异（LD）序列——以取代传统的伪随机性。这一转变同时提升了基于SC计算的精度和效率。我们通过对多种函数类型（包括三角函数、双曲函数和激活函数）的广泛实验验证了所提方法。与现有最优解决方案相比，所提出的设计方法将均方误差降低了高达98%，同时硬件面积、功耗和能耗分别减少了33%、72%和64%。