NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space

• URL: http://arxiv.org/abs/2608.13293v1
• Date: Thu, 13 Aug 2026 14:26:08 GMT
• Title: NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
• Abstract summary: Edge AI deployment demands neural architectures that are simultaneously accurate, computationally efficient, and hardware-deployable.<n>NAS is a challenge addressed by hardware-aware Neural Architecture Search (NAS)<n>Recent works incorporate quantization directly into the NAS loop, but these approaches expand search complexity and tightly couple architecture and quantization design.<n>No framework combines quantized architecture mapping onto reconfigurable accelerators with automated hardware exploration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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