面向边缘AI的NAS驱动硬件加速器探索及量化对帕累托空间的影响
NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
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中文总结 AI 辅助
本文针对边缘AI部署需求，提出结合NAS、量化与可重构加速器探索的三阶段流水线，实证分析INT4 PTQ对NAS帕累托空间的影响，发现FP32零样本代理覆盖度更优。
中文摘要 AI 辅助
边缘AI部署需要同时具备高准确率、计算高效性和硬件可部署性的神经架构，这一挑战由硬件感知的神经架构搜索（NAS）解决。尽管近期研究将量化直接纳入NAS循环，但这些方法会增加搜索复杂度并紧密耦合架构与量化设计。更简单的搜索后量化策略却鲜少得到分析关注：训练后量化（PTQ）对NAS发现的帕累托结构的影响尚未明确，且尚无框架将量化架构映射到可重构加速器与自动化硬件探索相结合。本文解决这两个缺口：首先，提出三阶段流水线：基于NAS-Bench-201的硬件无关帕累托排名代理前端、具备帕累托感知过滤与反馈控制的量化桥梁，以及基于CGRA4ML的进化域空间探索（DSE）后端以实现最优硬件映射；其次，开展实证研究，通过对15625个架构的真实数据采用形式化稳定性指标，表征INT4 PTQ如何扰动NAS-Bench-201的帕累托空间，并证明在两种标准搜索策略下，FP32零样本代理在帕累托空间覆盖度上优于专用INT4训练的代理。
英文摘要
Edge AI deployment demands neural architectures that are simultaneously accurate, computationally efficient, and hardware-deployable - a challenge addressed by hardware-aware Neural Architecture Search (NAS). While recent works incorporate quantization directly into the NAS loop, these approaches expand search complexity and tightly couple architecture and quantization design. The simpler post-search quantization strategy has received little analytical attention: the effects of Post-Training Quantization (PTQ) on the NAS-discovered Pareto structure remain uncharacterised, and no framework combines quantized architecture mapping onto reconfigurable accelerators with automated hardware exploration. This paper addresses both gaps. First, a three-stage pipeline is proposed: a hardware-agnostic Pareto rank surrogate frontend on NAS-Bench-201, a quantization bridge with Pareto-aware filtering and feedback control, and an evolutionary Domain Space Exploration (DSE) backend on CGRA4ML for optimal hardware mapping. Second, an empirical study characterises how INT4 PTQ perturbs the NAS-Bench-201 Pareto space through formal stability metrics on ground-truth data for all 15,625 architectures, and demonstrates that an FP32 zero-shot surrogate outperforms a dedicated INT4-trained surrogate in Pareto space coverage across two standard search strategies.
发表机构
- University of Patras（帕特雷大学）
机构由 AI 辅助整理，请以论文原文为准。
