# EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search Source: [EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:45:08.233Z Authors: Qian Jiang, Xiaofan Zhang, Deming Chen, Minh N. Do, Raymond A. Yeh Published: 2021-11-24T06:45:30.000Z ## Abstract In hardware-aware Differentiable Neural Architecture Search (DNAS), it is challenging to compute gradients of hardware metrics to perform architecture search. Existing works rely on linear approximations with limited support to customized hardware accelerators. In this work, we propose End-to-end Hardware-aware DNAS (EH-DNAS), a seamless integration of end-to-end hardware benchmarking, and fully automated DNAS to deliver hardware-efficient deep neural networks on various platforms, including Edge GPUs, Edge TPUs, Mobile CPUs, and customized accelerators. Given a desired hardware platform, we propose to learn a differentiable model predicting the end-to-end hardware performance of neural network architectures for DNAS. We also introduce E2E-Perf, an end-to-end hardware benchmarking tool for customized accelerators. Experiments on CIFAR10 and ImageNet show that EH-DNAS improves the hardware performance by an average of $1.4\\times$ on customized accelerators and $1.6\\times$ on existing hardware processors while maintaining the classification accuracy. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-10 (Standard Performance)](): Image Classification; CIFAR-10; Latency (ms): 1.1. ## Original sources and implementation [Original paper]() [PDF]()