ParaPIM
Author:
Affiliation:
1. University of Central Florida
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3287624.3287644
Reference24 articles.
1. 2011. NCSU EDA FreePDK45. http://www.eda.ncsu.edu/wiki/FreePDK45:Contents 2011. NCSU EDA FreePDK45. http://www.eda.ncsu.edu/wiki/FreePDK45:Contents
2. Renzo Andri et al. 2016. YodaNN: An ultra-low power convolutional neural network accelerator based on binary weights. In ISVLSI. IEEE 236--241. Renzo Andri et al. 2016. YodaNN: An ultra-low power convolutional neural network accelerator based on binary weights. In ISVLSI. IEEE 236--241.
3. CMP-PIM
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1. FAT: An In-Memory Accelerator With Fast Addition for Ternary Weight Neural Networks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-03
2. GCIM: Toward Efficient Processing of Graph Convolutional Networks in 3D-Stacked Memory;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2022-11
3. pLUTo: Enabling Massively Parallel Computation in DRAM via Lookup Tables;2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO);2022-10
4. Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation;2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO);2022-10
5. BioHD;Proceedings of the 49th Annual International Symposium on Computer Architecture;2022-06-11
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