Design, Model, and Explore Approximate Arithmetic Operators with AI/ML: A Tutorial

Abstract

Approximate Computing (AxC) is being actively explored to meet the energy and performance requirements of resource-constrained embedded systems. Approximate arithmetic operators (AxOs), for instance, let edge-AI systems trade tiny, bounded errors for big wins in power, performance, and area. This tutorial demystifies AxO design, modeling, and exploration: from platform-aware operator synthesis (e.g., selective LUT pruning) to application-specific DSE that uses AI/ML to navigate massive trade-off spaces. We contrast selection (library) vs. synthesis (generate-and-optimize) flows, show when FPGA-aware adders/multipliers outperform ASIC-ported designs, and connect operator-level error to task-level metrics (e.g., Conv2D, MLP). The tutorial includes hands-on Jupyter notebooks, ready-to-reuse operator models, and a practical recipe for building Pareto-optimal AxOs under accuracy constraints

plus a peek at AxOSyn, an open-source framework that unifies selection/synthesis, surrogate fitness, and search using evolutionary algorithms.