Results 11 to 20 of about 7,279,238 (253)
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
CoRR, 2021 Submitted to Proceedings of ...
Hadjer Benmeziane +5 more
core +6 more sources
A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities [PDF]
CoRR, 2022 Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable effort has been employed towards improving the first stage, namely, the training of the super-network, the search for derivative high ...
Daniel Cummings +5 more
openaire +3 more sources
Hardware-Aware Neural Architecture Search
319347The manual search for optimal neural network topologies, like maximizing the accuracy or minimizing the latency, is a time-consuming process that often requires multiple iterations. Depending on the use case, several target objectives are optimized
Loroch, Dominik
core +3 more sources
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices [PDF]
IEEE Trans. ComputersGraph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Nevertheless, the research community has primarily focused on improving model expressiveness, lacking consideration of how to design efficient GNN models for edge scenarios with ...
Ao Zhou +7 more
core +5 more sources
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference13 pages, 9 ...
Halima Bouzidi +3 more
core +4 more sources
Applied SciencesMedical endoscopic video processing requires real-time execution of color component acquisition, color filter array (CFA) demosaicing, and high dynamic range (HDR) compression under low-light conditions, while adhering to strict thermal constraints ...
Cunguang Zhang +7 more
doaj +2 more sources
Hardware-Aware Bayesian Neural Architecture Search of Quantized CNNs
IEEE Embedded Systems LettersAdvances in Neural Architecture Search (NAS) now provide a crucial assistance to design Hardware-efficient neural networks. This paper presents NAS for resource-efficient, weight-quantized Convolutional Neural Networks (CNNs), under computational complexity constraints (model size and number of computations).
Perrin, Mathieu +3 more
openaire +4 more sources
Hardware-Aware Neural Architecture Search: Survey and Taxonomy [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 There is no doubt that making AI mainstream by bringing powerful, yet power hungry deep neural networks (DNNs) to resource-constrained devices would required an efficient co-design of algorithms, hardware and software. The increased popularity of DNN applications deployed on a wide variety of platforms, from tiny microcontrollers to data-centers, have ...
Hadjer Benmeziane +5 more
openaire +1 more source
Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference
Frontiers in Artificial Intelligence, 2021 Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption.
Benjamin Hawks +6 more
doaj +1 more source
IEEE Access, 2022 Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks.
Alberto Marchisio +5 more
doaj +1 more source