145 citations · 981 across the 31 of their papers we have counts for
4 papers · 1 filter
Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays
Hyungjun Kim, Malte Rasch, Tayfun Gokmen +5
A resistive memory device-based computing architecture is one of the promising platforms for energy-efficient Deep Neural Network (DNN) training accelerators. The key technical cha…
Design and Characterization of Superconducting Nanowire-Based Processors for Acceleration of Deep Neural Network Training
Murat Onen, Brenden A. Butters, Emily Toomey +2
Training of deep neural networks (DNNs) is a computationally intensive task and requires massive volumes of data transfer. Performing these operations with the conventional von Neu…
Efficient ConvNets for Analog Arrays
Malte J. Rasch, Tayfun Gokmen, Mattia Rigotti +1
Analog arrays are a promising upcoming hardware technology with the potential to drastically speed up deep learning. Their main advantage is that they compute matrix-vector product…
Analog CMOS-based Resistive Processing Unit for Deep Neural Network Training
Seyoung Kim, Tayfun Gokmen, Hyung-Min Lee +1
Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training…