The rising usage of AI and ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded systems. To this end, approximate computing, an approach that explores the power, performance, area (PPA), and behavioral accuracy (BEHAV) trade-offs, has emerged as a possible solution for implementing embedded machine learning. Due to the predominance of MAC operations in ML, designing platform-specific approximate arithmetic operators forms one of the major research problems in approximate computing. Recently there has been a rising usage of AI/ML-based design space exploration techniques for implementing approximate operators. However, most of these approaches are limited to using ML-based surrogate functions for predicting the PPA and BEHAV impact of a set of related design decisions. While this approach leverages the regression capabilities of ML methods, it does not exploit the more advanced approaches in ML. To this end, we propose AxOCS, a methodology for designing approximate arithmetic operators through ML-based supersampling. Specifically, we present a method to leverage the correlation of PPA and BEHAV metrics across operators of varying bit-widths for generating larger bit-width operators. The proposed approach involves traversing the relatively smaller design space of smaller bit-width operators and employing its associated Design-PPA-BEHAV relationship to generate initial solutions for metaheuristics-based optimization for larger operators. The experimental evaluation of AxOCS for FPGA-optimized approximate operators shows that the proposed approach significantly improves the quality-resulting hypervolume for multi-objective optimization-of 8x8 signed approximate multipliers.

翻译：随着AI和ML处理在应用领域的广泛使用，低成本机器学习实现（尤其是资源受限嵌入式系统）的需求日益迫切。为此，近似计算——一种探索功耗、性能、面积与行为精度之间权衡的方法——已成为实现嵌入式机器学习的潜在解决方案。由于ML中乘积累加运算的主导地位，设计面向特定平台的近似算术算子成为近似计算领域的主要研究问题之一。近年来，基于AI/ML的设计空间探索技术被越来越多地用于实现近似算子。然而，大多数方法仅局限于利用ML代理函数预测相关设计决策对PPA和BEHAV的影响。虽然这类方法发挥了ML方法的回归能力，但未能充分利用ML中更高级的技术。为此，本文提出AxOCS——一种基于ML超采样技术的近似算术算子设计方法。具体而言，我们提出利用不同位宽算子间PPA和BEHAV指标的关联性来生成更大位宽算子。该方法通过遍历较小位宽算子的相对小型设计空间，利用其设计-PPA-BEHAV关系为基于元启发式的大算子优化生成初始解。针对FPGA优化近似算子的实验评估表明，所提方法显著提升了8x8有符号近似乘法器多目标优化的质量超体积指标。