11 citations · 33 across the 10 of their papers we have counts for
10 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…
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…
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…
On Value Recomputation to Accelerate Invisible Speculation
Christos Sakalis, Zamshed I. Chowdhury, Shayne Wadle +5
Recent architectural approaches that address speculative side-channel attacks aim to prevent software from exposing the microarchitectural state changes of transient execution. The…
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…
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…
Read Mapping Near Non-Volatile Memory
S. Karen Khatamifard, Zamshed Chowdhury, Nakul Pande +3
DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology…