49 citations · 94 across the 13 of their papers we have counts for
14 papers
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…
Hypothesis Testing for Error Mitigation: How to Evaluate Error Mitigation
Abdullah Ash Saki, Amara Katabarwa, Salonik Resch +1
In the noisy intermediate-scale quantum (NISQ) era, quantum error mitigation will be a necessary tool to extract useful performance out of quantum devices. However, there is a big…
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…
Towards Homomorphic Inference Beyond the Edge
Salonik Resch, Zamshed I. Chowdhury, Husrev Cilasun +5
Beyond edge devices can function off the power grid and without batteries, enabling them to operate in difficult to access regions. However, energy costly long-distance communicati…
Effectiveness of Variable Distance Quantum Error Correcting Codes
Salonik Resch, Ulya R. Karpuzcu
Quantum error correction is capable of digitizing quantum noise and increasing the robustness of qubits. Typically, error correction is designed with the target of eliminating all…
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…
Exploring the Feasibility of Using 3D XPoint as an In-Memory Computing Accelerator
Masoud Zabihi, Salonik Resch, Husrev Cılasun +5
This paper describes how 3D XPoint memory arrays can be used as in-memory computing accelerators. We first show that thresholded matrix-vector multiplication (TMVM), the fundamenta…
Introductory Tutorial for SPSA and the Quantum Approximation Optimization Algorithm
Salonik Resch
This short tutorial provides an introduction to the Quantum Approximation Optimization Algorithm (QAOA). Specifically, how to use QAOA with the Simultaneous Perturbation Stochastic…
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…
Benchmarking Quantum Computers and the Impact of Quantum Noise
Salonik Resch, Ulya R. Karpuzcu
Benchmarking is how the performance of a computing system is determined. Surprisingly, even for classical computers this is not a straightforward process. One must choose the appro…
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…
Quantum Computing: An Overview Across the System Stack
Salonik Resch, Ulya R. Karpuzcu
Quantum computers, if fully realized, promise to be a revolutionary technology. As a result, quantum computing has become one of the hottest areas of research in the last few years…
Computational RAM to Accelerate String Matching at Scale
Zamshed I. Chowdhury, S. Karen Khatamifard, Zhengyang Zhao +6
Traditional Von Neumann computing is falling apart in the era of exploding data volumes as the overhead of data transfer becomes forbidding. Instead, it is more energy-efficient to…
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…