16 citations · 76 across the 16 of their papers we have counts for
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cs.AI2021★ 3 cited
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
Sian Jin, Chengming Zhang, Xintong Jiang +5
Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during…
cs.LG2021
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
Geng Yuan, Zhiheng Liao, Xiaolong Ma +11
Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vecto…
cs.DC2021★ 8 cited
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression
Chengming Zhang, Sian Jin, Tong Geng +3
As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounde…