mlsanityData to 14 Oct 2026

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search

arXiv 2111.12299 ↗First posted 24 Nov 2021cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

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× on customized accelerators and 1.6× on existing hardware processors while maintaining the classification accuracy.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 24 Nov 2021.

Builds on

34 of 47 references.

References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  6. 2014Going Deeper with Convolutionscited by 4,241
  7. 2019Searching for MobileNetV3cited by 1,994
  8. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  9. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  10. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  11. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  12. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
22 more
  1. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  2. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
  3. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 618
  4. 2017Progressive Neural Architecture Searchcited by 487
  5. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 485
  6. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 416
  7. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 344
  8. 2018SNAS: Stochastic Neural Architecture Searchcited by 289
  9. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 279
  10. 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 260
  11. 2020FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensionscited by 152
  12. 2020BRP-NAS: Prediction-based NAS using GCNscited by 111
  13. 2020NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Sizecited by 98
  14. 2019Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hourscited by 87
  15. 2021HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmarkcited by 84
  16. 2018ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptationcited by 76
  17. 2019FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edgecited by 48
  18. 2019SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systemscited by 27
  19. 2020AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICscited by 26
  20. 2020Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Startcited by 22
  21. 2020DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architecturescited by 16
  22. 2020HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementationcited by 9

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Searchcited by 5
  2. 2023Search-time Efficient Device Constraints-Aware Neural Architecture Searchcited by 3
  3. 2024Multi-objective Differentiable Neural Architecture Searchcited by 1
  4. 2025Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systemscited by 0