Neural Architecture Search for Intel Movidius VPU
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Abstract (arXiv)
Hardware-aware Neural Architecture Search (NAS) technologies have been proposed to automate and speed up model design to meet both quality and inference efficiency requirements on a given hardware. Prior arts have shown the capability of NAS on hardware specific network design. In this whitepaper, we further extend the use of NAS to Intel Movidius VPU (Vision Processor Units). To determine the hardware-cost to be incorporated into the NAS process, we introduced two methods: pre-collected hardware-cost on device and device-specific hardware-cost model VPUNN. With the help of NAS, for classification task on VPU, we can achieve 1.3x fps acceleration over Mobilenet-v2-1.4 and 2.2x acceleration over Resnet50 with the same accuracy score. For super resolution task on VPU, we can achieve 1.08x PSNR and 6x higher fps compared with EDSR3.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 9 references that are in the map. First posted on arXiv 5 May 2023.
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References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
- 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
- 2021HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmarkcited by 84
- 2021A Comprehensive Survey on Hardware-Aware Neural Architecture Searchcited by 61
- 2019Fast Hardware-Aware Neural Architecture Searchcited by 23
- 2022Towards Optimal VPU Compiler Cost Modeling by using Neural Networks to Infer Hardware Performancescited by 2
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