7 citations · 7 across the 2 of their papers we have counts for
3 papers
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…
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…
Post-Training Piecewise Linear Quantization for Deep Neural Networks
Jun Fang, Ali Shafiee, Hamzah Abdel-Aziz +3
Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices. Post-training quantization is highly desirable since it…