Hardware Aware Neural Architecture Search
7 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Hardware Aware Neural Architecture Search
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Visual Wake Words
Most implemented papers
Fast Hardware-Aware Neural Architecture Search
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.
HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
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).
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures.
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search
Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference.
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
alibaba/lightweight-neural-architecture-search
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Conference on Neural Information Processing Systems 2022
Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage.
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor
The current trend of applying transfer learning from convolutional neural networks (CNNs) trained on large datasets can be an overkill when the target application is a custom and delimited problem, with enough data to train a network from scratch.
On Latency Predictors for Neural Architecture Search
abdelfattah-lab/nasflat_latency
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We then design a general latency predictor to comprehensively study (1) the predictor architecture, (2) NN sample selection methods, (3) hardware device representations, and (4) NN operation encoding schemes.
