2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) 2022
DOI: 10.1109/vlsitechnologyandcir46769.2022.9830487
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A 40-nm 646.6TOPS/W Sparsity-Scaling DNN Processor for On-Device Training

Zih-Sing Fu
1
,
Yu‐Chi Lee
2
,
Alex Park
3
et al.
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A 28-nm 8-bit Floating-Point Tensor Core-Based Programmable CNN Training Processor With Dynamic Structured Sparsity

Venkataramanaiah
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,
Meng
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et al. 2023
IEEE J. Solid-State Circuits
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A 28-nm 8-bit Floating-Point Tensor Core-Based Programmable CNN Training Processor With Dynamic Structured Sparsity

Venkataramanaiah
1
,
Meng
2
,
Suh
3
et al. 2023
IEEE J. Solid-State Circuits
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DQ-STP: An Efficient Sparse On-Device Training Processor Based on Low-Rank Decomposition and Quantization for DNN

Li,
Zhang,
Zhao
et al. 2024
IEEE Trans. Circuits Syst. I
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ACBN: Approximate Calculated Batch Normalization for Efficient DNN On-Device Training Processor

Li
1
,
Wang
2
,
Luo
3
et al. 2023
IEEE Trans. VLSI Syst.
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