8 citations · 9 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 8 cited
Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators
Noah Gamboa, Kais Kudrolli, Anand Dhoot +1
This paper studies structured sparse training of CNNs with a gradual pruning technique that leads to fixed, sparse weight matrices after a set number of epochs. We simplify the str…
cs.DC2017★ 1 cited
CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks
Yuanfang Li, Ardavan Pedram
Accelerating the inference of a trained DNN is a well studied subject. In this paper we switch the focus to the training of DNNs. The training phase is compute intensive, demands c…
cs.DC2016
A Systematic Approach to Blocking Convolutional Neural Networks
Xuan Yang, Jing Pu, Blaine Burton Rister +6
Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations. Most impleme…