49 citations · 94 across the 13 of their papers we have counts for
6 papers · 1 filter
Experimental demonstration of magnetic tunnel junction-based computational random-access memory
Yang Lv, Brandon R. Zink, Robert P. Bloom +9
Conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence, because much of the power an…
On Error Correction for Nonvolatile Processing-In-Memory
Hüsrev Cılasun, Salonik Resch, Zamshed I. Chowdhury +6
Processing in memory (PiM) represents a promising computing paradigm to enhance performance of numerous data-intensive applications. Variants performing computing directly in emerg…
Accelerating Variational Quantum Algorithms Using Circuit Concurrency
Salonik Resch, Anthony Gutierrez, Joon Suk Huh +5
Variational quantum algorithms (VQAs) provide a promising approach to achieve quantum advantage in the noisy intermediate-scale quantum era. In this era, quantum computers experien…
An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)
Hüsrev Cılasun, Salonik Resch, Zamshed I. Chowdhury +8
Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise…
A Machine Learning Accelerator In-Memory for Energy Harvesting
Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury +5
There is increasing demand to bring machine learning capabilities to low power devices. By integrating the computational power of machine learning with the deployment capabilities…
PIMBALL: Binary Neural Networks in Spintronic Memory
Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury +5
Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for perform…