Hardware Aware Neural Architecture Search

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (37 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Optimizing edge AI models on HPC systems with the edge in the loop
  26 May 2025 1 repository listed
  Artificial intelligence and machine learning models deployed on edge devices, e.
• MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
  15 May 2025 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  The growing use of smartphones and IoT devices necessitates efficient time-series analysis on resource-constrained hardware, which is critical for sensing applications such as human activity recognition and air quality…
• TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification
  29 Aug 2024 1 repository listed
  In this work, we present TinyTNAS, a novel hardware-aware multi-objective Neural Architecture Search (NAS) tool specifically designed for TinyML time series classification.
• A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
  25 Apr 2024 1 repository listed
  To bridge the gap, we introduce a tailored streamline to transform the task of HW-NAS for real-time semantic segmentation into standard MOPs.
• On Latency Predictors for Neural Architecture Search
  4 Mar 2024 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  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.
• Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor
  15 Dec 2022 1 repository listed
  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…
• Entropy-Driven Mixed-Precision Quantization for Deep Network Design
  28 Nov 2022 1 repository listed
  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.
• U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search
  23 Mar 2022 1 repository listed
  Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference.
• One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
  1 Nov 2021 1 repository listed
  A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures.
• HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
  19 Mar 2021 1 repository listed
  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…
• Work-in-progress: a deep learning strategy for I/O scheduling in storage systems
  3 Dec 2019 1 repository listed
  Under the big data era, there is a crucial need to improve the performance of storage systems for data-intensive applications.
• Fast Hardware-Aware Neural Architecture Search
  25 Oct 2019 1 repository listed
  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…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.