Open Access
Issue |
JNWPU
Volume 43, Number 1, February 2025
|
|
---|---|---|
Page(s) | 119 - 127 | |
DOI | https://doi.org/10.1051/jnwpu/20254310119 | |
Published online | 18 April 2025 |
- LEE J, KANG S, LEE J, et al. The hardware and algorithm co-design for energy-efficient dnn processor on edge/mobile devices[J]. IEEE Trans on Circuits and Systems, 2020, 67(10): 3458–3470. [Article] [Google Scholar]
- CHOI S, SHIN J, KIM L S. A convergence monitoring method for DNN training of on-device task adaptation[C]//2021 IEEE/ACM International Conference on Computer Aided Design, 2021: 1–9 [Google Scholar]
- CHOI S, SHIN J, KIM L S. Accelerating on-device DNN training workloads via runtime convergence monitor[J]. IEEE Trans on Computer-Aided Design of Integrated Circuits and Systems, 2023, 42(5): 1574–1587. [Article] [Google Scholar]
- WANG Z H. Efficient on-device incremental learning by weight freezing[C]//2022 27th Asia and South Pacific Design Automation Conference, 2022: 538–543 [Google Scholar]
- LI B. DQ-STP: an efficient sparse on-device training processor based on low-rank decomposition and quantization for DNN[J]. IEEE Trans on Circuits and Systems, 2024, 71(4): 1665–1678. [Article] [Google Scholar]
- LU J, HUANG J, WANG Z. Theta: a high-efficiency training accelerator for dnns with triple-side sparsity exploration[J]. IEEE Trans on Very Large Scale Integration Systems, 2022, 30(8): 1034–1046 [Google Scholar]
- YANG D, GHASEMAZAR A, REN X, et al. Procrustes: a dataflow and accelerator for sparse deep neural network training[C]//2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture, 2020: 711–724 [Google Scholar]
- GONDIMALLA A, et al. SparTen: sparse tensor accelerator for convolutional neural networks[C]//Proceedings of the 52nd Annual IEEE/ACM International Symposium on Micnarchitecture, 2019: 151–165 [Google Scholar]
- MAHMOUD M, EDO I, ZADEH A H, et al. Tensordash: exploiting sparsity to accelerate deep neural network training[C]//2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture, 2020: 781–795 [Google Scholar]
- NVIDIA. NVIDIA ampere GA102 GPU architecture whitepaper[EB/OL]. (2020-09-16)[2024-10-09]. https://www.nvidia.com/content/PDF/nvidia-ampere-ge-102-gpu-architecture-whitepaper-v2.pdf [Google Scholar]
- LIU Z, WHATMOUGH P N, ZHU Y, et al. S2TA: exploiting structured sparsity for energy-efficient mobile CNN acceleration[C]//2022 IEEE International Symposium on High-Performance Computer Architecture, 2022: 573–586 [Google Scholar]
- WANG M, FAN X, ZHANG W, et al. Balancing memory-accessing and computing over sparse DNN accelerator via efficient data packaging[J]. Journal of Systems Architecture, 2021, 117(C): 102094. [Article] [Google Scholar]
- LEE G, PARK H, KIM N, et al. : Acceleration of DNN backward propagation by selective computation of gradients[C]//2019 56th ACM/IEEE Design Automation Conference, 2019: 1–6 [Google Scholar]
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