Nonlinearities are crucial for capturing complex input-output relationships
especially in deep neural networks. However, nonlinear functions often incur
various hardware and compute overheads. Meanwhile, stochastic computing (SC)
has emerged as a promising approach to tackle this challenge by trading output
precision for hardware simplicity. To this end, this paper proposes a
first-of-its-kind stochastic multivariate universal-radix finite-state machine
(SMURF) that harnesses SC for hardware-simplistic multivariate nonlinear
function generation at high accuracy. We present the finite-state machine (FSM)
architecture for SMURF, as well as analytical derivations of sampling gate
coefficients for accurately approximating generic nonlinear functions.
Experiments demonstrate the superiority of SMURF, requiring only 16.07% area
and 14.45% power consumption of Taylor-series approximation, and merely 2.22%
area of look-up table (LUT) schemes.

基于随机计算的多元非线性函数生成的硬件简化的多元通用基数有限状态机 (SMURF) 及其架构、采样门系数的分析推导以及与 Taylor 级数逼近和查找表方案相比的优越性实验证明。