16 citations · 32 across the 4 of their papers we have counts for
3 papers · 1 filter
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
Jong Hoon Shin, Ali Shafiee, Ardavan Pedram +3
This paper examines the design space trade-offs of DNNs accelerators aiming to achieve competitive performance and efficiency metrics for all four combinations of dense or sparse a…
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Geng Yuan, Payman Behnam, Zhengang Li +8
Recent works demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector m…
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
Hamzah Abdel-Aziz, Ali Shafiee, Jong Hoon Shin +2
In this paper, we propose a mixed-precision convolution unit architecture which supports different integer and floating point (FP) precisions. The proposed architecture is based on…