Accelerator-aware Neural Network Design using AutoML

• URL: http://arxiv.org/abs/2003.02838v1
• Date: Thu, 5 Mar 2020 21:34:22 GMT
• Title: Accelerator-aware Neural Network Design using AutoML
• Authors: Suyog Gupta, Berkin Akin
• Abstract summary: We present a class of computer vision models designed using hardware-aware neural architecture search and customized to run on the Edge TPU. For the Edge TPU in Coral devices, these models enable real-time image classification performance while achieving accuracy typically seen only with larger, compute-heavy models running in data centers.
• Score: 5.33024001730262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While neural network hardware accelerators provide a substantial amount of raw compute throughput, the models deployed on them must be co-designed for the underlying hardware architecture to obtain the optimal system performance. We present a class of computer vision models designed using hardware-aware neural architecture search and customized to run on the Edge TPU, Google's neural network hardware accelerator for low-power, edge devices. For the Edge TPU in Coral devices, these models enable real-time image classification performance while achieving accuracy typically seen only with larger, compute-heavy models running in data centers. On Pixel 4's Edge TPU, these models improve the accuracy-latency tradeoff over existing SoTA mobile models.

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