mlsanityData to 7 Oct 2026

HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark

arXiv 2103.10584 ↗First posted 19 Mar 2021cs.LGcited by 83

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Abstract (arXiv)

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of DNNs deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to determine the hardware-cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected hardware-cost look-up tables or device-specific hardware-cost models. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to their significant required computational resources and the differences in adopted search spaces, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases to (1) show that HW-NAS-Bench allows non-hardware experts to perform HW-NAS by simply querying it and (2) verify that dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost trade-offs. The codes and all collected data are available at https://github.com/RICE-EIC/HW-NAS-Bench.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). Builds on 38 of its 72 references that are in the map. First posted on arXiv 19 Mar 2021.

Builds on

38 of 72 references.

References · 38

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  4. 2019Searching for MobileNetV3cited by 1,984
  5. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  6. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  7. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  8. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 937
  9. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  10. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  11. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 412
  12. 2017A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetscited by 374
26 more
  1. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
  2. 2020FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensionscited by 152
  3. 2020BRP-NAS: Prediction-based NAS using GCNscited by 109
  4. 2020NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Sizecited by 98
  5. 2019FastDepth: Fast Monocular Depth Estimation on Embedded Systemscited by 93
  6. 2019Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hourscited by 87
  7. 2019NAS evaluation is frustratingly hardcited by 69
  8. 2020GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernetcited by 69
  9. 2020NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processingcited by 60
  10. 2020MobileDets: Searching for Object Detection Architectures for Mobile Acceleratorscited by 55
  11. 2020ShiftAddNet: A Hardware-Inspired Deep Networkcited by 52
  12. 2020AutoGAN-Distiller: Searching to Compress Generative Adversarial Networkscited by 50
  13. 2019E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savingscited by 36
  14. 2018Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutionscited by 33
  15. 2019Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inferencecited by 31
  16. 2016Maximizing CNN Accelerator Efficiency Through Resource Partitioningcited by 29
  17. 2019daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devicescited by 27
  18. 2020Fractional Skipping: Towards Finer-Grained Dynamic CNN Inferencecited by 23
  19. 2020DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architecturescited by 16
  20. 2020SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computationcited by 16
  21. 2020NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networkscited by 16
  22. 2020Discovering Multi-Hardware Mobile Models via Architecture Searchcited by 12
  23. 2020DNA: Differentiable Network-Accelerator Co-Searchcited by 11
  24. 2016A Systematic Approach to Blocking Convolutional Neural Networkscited by 7
  25. 2020S3NAS: Fast NPU-aware Neural Architecture Search Methodologycited by 5
  26. 2020Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Searchcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 16; 2023: 13; 2024: 18; 2025: 12; 2026: 13.

83 papers.

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  1. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  2. 2024LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insightscited by 65
  3. 2021MQBench: Towards Reproducible and Deployable Model Quantization Benchmarkcited by 38
  4. 2023A Survey of Techniques for Optimizing Transformer Inferencecited by 35
  5. 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
  6. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  7. 2023Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunitiescited by 21
  8. 2022DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networkscited by 19
  9. 2022Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessmentcited by 17
  10. 2024ParetoFlow: Guided Flows in Multi-Objective Optimizationcited by 17
  11. 2021HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learningcited by 16
  12. 2022Learning Where To Look -- Generative NAS is Surprisingly Efficientcited by 15
38 more
  1. 2023DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Modelscited by 15
  2. 2024Offline Multi-Objective Optimizationcited by 14
  3. 2021Automated Deep Learning: Neural Architecture Search Is Not the Endcited by 13
  4. 2021SPIDER: Searching Personalized Neural Architecture for Federated Learningcited by 13
  5. 2024Multi-conditioned Graph Diffusion for Neural Architecture Searchcited by 13
  6. 2021A multi-objective perspective on jointly tuning hardware and hyperparameterscited by 12
  7. 2025Preference-Guided Diffusion for Multi-Objective Offline Optimizationcited by 12
  8. 2022A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devicescited by 11
  9. 2022EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Searchcited by 11
  10. 2024Surprisingly Strong Performance Prediction with Neural Graph Featurescited by 11
  11. 2021DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systemscited by 10
  12. 2024HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Modelscited by 10
  13. 2021NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameterscited by 9
  14. 2021One Proxy Device Is Enough for Hardware-Aware Neural Architecture Searchcited by 8
  15. 2024SeqNAS: Neural Architecture Search for Event Sequence Classificationcited by 8
  16. 2024On Latency Predictors for Neural Architecture Searchcited by 7
  17. 2024Model Parallelism on Distributed Infrastructure: A Literature Review from Theory to LLM Case-Studiescited by 7
  18. 2023Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Searchcited by 6
  19. 2023ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Designcited by 6
  20. 2022CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Frameworkcited by 5
  21. 2023TFormer: A Transmission-Friendly ViT Model for IoT Devicescited by 5
  22. 2023Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresencecited by 5
  23. 2024Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversitycited by 5
  24. 2024CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Frameworkcited by 5
  25. 2021Bag of Tricks for Neural Architecture Searchcited by 4
  26. 2021EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Searchcited by 4
  27. 2022Pareto-aware Neural Architecture Generation for Diverse Computational Budgetscited by 4
  28. 2024MONAS: Efficient Zero-Shot Neural Architecture Search for MCUscited by 4
  29. 2022Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamicscited by 3
  30. 2022B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Searchcited by 3
  31. 2022MAPLE-Edge: A Runtime Latency Predictor for Edge Devicescited by 3
  32. 2022Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computingcited by 3
  33. 2023Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importancecited by 3
  34. 2025Energy-Aware Deep Learning on Resource-Constrained Hardwarecited by 3
  35. 2026QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networkscited by 3
  36. 2023Carbon-Efficient Neural Architecture Searchcited by 2
  37. 2025MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devicescited by 2
  38. 2025FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Reviewcited by 2

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