145 citations · 981 across the 31 of their papers we have counts for
32 papers
Fast offset corrected in-memory training
Malte J. Rasch, Fabio Carta, Omebayode Fagbohungbe +1
In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory c…
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…
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
Malte J. Rasch, Diego Moreda, Tayfun Gokmen +6
We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch…
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…
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…