344 citations · 1.3k across the 15 of their papers we have counts for
16 papers
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Wilfried Haensch, Anand Raghunathan, Kaushik Roy +4
Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of tod…
Neural Network Training with Asymmetric Crosspoint Elements
Murat Onen, Tayfun Gokmen, Teodor K. Todorov +5
Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymm…
Algorithm for Training Neural Networks on Resistive Device Arrays
Tayfun Gokmen, Wilfried Haensch
Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic…
Training large-scale ANNs on simulated resistive crossbar arrays
Malte J. Rasch, Tayfun Gokmen, Wilfried Haensch
Accelerating training of artificial neural networks (ANN) with analog resistive crossbar arrays is a promising idea. While the concept has been verified on very small ANNs and toy…
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…
Training LSTM Networks with Resistive Cross-Point Devices
Tayfun Gokmen, Malte Rasch, Wilfried Haensch
In our previous work we have shown that resistive cross point devices, so called Resistive Processing Unit (RPU) devices, can provide significant power and speed benefits when trai…