# Fast Hardware-Aware Neural Architecture Search

arXiv: https://arxiv.org/abs/1910.11609 · HTML: https://mlsanity.com/paper/1910.11609/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 23 of 23)

- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [Hessian-Aware Pruning and Optimal Neural Implant](https://mlsanity.com/paper/2101.08940/) (2021)
- [Searching for Fast Model Families on Datacenter Accelerators](https://mlsanity.com/paper/2102.05610/) (2021)
- [ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices](https://mlsanity.com/paper/2303.09730/) (2023)
- [MAPLE: Microprocessor A Priori for Latency Estimation](https://mlsanity.com/paper/2111.15106/) (2021)
- [Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search](https://mlsanity.com/paper/2304.05405/) (2023)
- [Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search](https://mlsanity.com/paper/2109.12426/) (2021)
- [RHNAS: Realizable Hardware and Neural Architecture Search](https://mlsanity.com/paper/2106.09180/) (2021)
- [U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search](https://mlsanity.com/paper/2203.12412/) (2022)
- [High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-World Manufacturing Environments](https://mlsanity.com/paper/2212.10632/) (2022)
- [MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks](https://mlsanity.com/paper/2502.07422/) (2025)
- [Searching Neural Architectures for Sensor Nodes on IoT Gateways](https://mlsanity.com/paper/2505.23939/) (2025)
- [End-to-End Neural Network Compression via fracℓ1ℓ2 Regularized Latency Surrogates](https://mlsanity.com/paper/2306.05785/) (2023)
- [Multi-objective Differentiable Neural Architecture Search](https://mlsanity.com/paper/2402.18213/) (2024)
- [Combining Neural Architecture Search and Automatic Code Optimization: A Survey](https://mlsanity.com/paper/2408.04116/) (2024)
- [CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads](https://mlsanity.com/paper/2409.01089/) (2024)
- [SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation](https://mlsanity.com/paper/2304.11207/) (2023)
- [Neural Architecture Search for Intel Movidius VPU](https://mlsanity.com/paper/2305.03739/) (2023)
- [A Survey on Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.09099/) (2023)
- [Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost](https://mlsanity.com/paper/2408.02280/) (2024)
- [On-Device Neural Architecture Search](https://mlsanity.com/paper/2606.24900/) (2026)
- [Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras](https://mlsanity.com/paper/2607.28293/) (2026)
- [Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study](https://mlsanity.com/paper/2609.30553/) (2026)

## Abstract (arXiv)

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35x inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49x speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.