For NAS-Bench-201, INT4 post-training quantization fully reorganizes the Pareto front, yet an FP32 zero-shot surrogate still achieves higher Pareto-space coverage than an INT4-trained surrogate.
NAAS: Neural Accelerator Architecture Search
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abstract
Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus on sizing the numerical architectural hyper-parameters while neglect searching the PE connectivities and compiler mappings. To tackle this challenge, we propose Neural Accelerator Architecture Search (NAAS) which holistically searches the neural network architecture, accelerator architecture, and compiler mapping in one optimization loop. NAAS composes highly matched architectures together with efficient mapping. As a data-driven approach, NAAS rivals the human design Eyeriss by 4.4x EDP reduction with 2.7% accuracy improvement on ImageNet under the same computation resource, and offers 1.4x to 3.5x EDP reduction than only sizing the architectural hyper-parameters.
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cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
For NAS-Bench-201, INT4 post-training quantization fully reorganizes the Pareto front, yet an FP32 zero-shot surrogate still achieves higher Pareto-space coverage than an INT4-trained surrogate.