10 citations · 15 across the 4 of their papers we have counts for
4 papers
Procrustes: a Dataflow and Accelerator for Sparse Deep Neural Network Training
Dingqing Yang, Amin Ghasemazar, Xiaowei Ren +3
The success of DNN pruning has led to the development of energy-efficient inference accelerators that support pruned models with sparse weight and activation tensors. Because the m…
TinBiNN: Tiny Binarized Neural Network Overlay in about 5,000 4-LUTs and 5mW
Guy G. F. Lemieux, Joe Edwards, Joel Vandergriendt +6
Reduced-precision arithmetic improves the size, cost, power and performance of neural networks in digital logic. In convolutional neural networks, the use of 1b weights can achieve…
Full deep neural network training on a pruned weight budget
Maximilian Golub, Guy Lemieux, Mieszko Lis
We introduce a DNN training technique that learns only a fraction of the full parameter set without incurring an accuracy penalty. To do this, our algorithm constrains the total nu…
Automated Space/Time Scaling of Streaming Task Graph
Hossein Omidian, Guy G. F. Lemieux
In this paper, we describe a high-level synthesis (HLS) tool that automatically allows area/throughput trade-offs for implementing streaming task graphs (STG). Our tool targets a m…