Neural Architecture Search of Hybrid Models for NPU-CIM Heterogeneous AR/VR Devices

• URL: http://arxiv.org/abs/2410.08326v1
• Date: Thu, 10 Oct 2024 19:30:34 GMT
• Title: Neural Architecture Search of Hybrid Models for NPU-CIM Heterogeneous AR/VR Devices
• Authors: Yiwei Zhao, Ziyun Li, Win-San Khwa, Xiaoyu Sun, Sai Qian Zhang, Syed Shakib Sarwar, Kleber Hugo Stangherlin, Yi-Lun Lu, Jorge Tomas Gomez, Jae-Sun Seo, Phillip B. Gibbons, Barbara De Salvo, Chiao Liu,
• Abstract summary: We introduce H4H-NAS, a Neural Architecture Search framework to design efficient hybrid CNN/ViT models for heterogeneous edge systems. Results from our Algo/HW co-design reveal up to 56.08% overall latency and 41.72% energy improvements.
• Score: 10.75997684204274
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Low-Latency and Low-Power Edge AI is essential for Virtual Reality and Augmented Reality applications. Recent advances show that hybrid models, combining convolution layers (CNN) and transformers (ViT), often achieve superior accuracy/performance tradeoff on various computer vision and machine learning (ML) tasks. However, hybrid ML models can pose system challenges for latency and energy-efficiency due to their diverse nature in dataflow and memory access patterns. In this work, we leverage the architecture heterogeneity from Neural Processing Units (NPU) and Compute-In-Memory (CIM) and perform diverse execution schemas to efficiently execute these hybrid models. We also introduce H4H-NAS, a Neural Architecture Search framework to design efficient hybrid CNN/ViT models for heterogeneous edge systems with both NPU and CIM. Our H4H-NAS approach is powered by a performance estimator built with NPU performance results measured on real silicon, and CIM performance based on industry IPs. H4H-NAS searches hybrid CNN/ViT models with fine granularity and achieves significant (up to 1.34%) top-1 accuracy improvement on ImageNet dataset. Moreover, results from our Algo/HW co-design reveal up to 56.08% overall latency and 41.72% energy improvements by introducing such heterogeneous computing over baseline solutions. The framework guides the design of hybrid network architectures and system architectures of NPU+CIM heterogeneous systems.

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