72 citations · 203 across the 31 of their papers we have counts for
11 papers · 1 filter
DROID: Discrete-Time Simulation for Ring-Oscillator-Based Ising Design
Abhimanyu Kumar, Ramprasath S., Chris H. Kim +2
Many combinatorial problems can be mapped to Ising machines, i.e., networks of coupled oscillators that settle to a minimum-energy ground state, from which the problem solution is…
HETRI: Heterogeneous Ising Multiprocessing
Ahmet Efe, Hüsrev Cılasun, Abhimanyu Kumar +4
Ising machines are effective solvers for complex combinatorial optimization problems. The idea is mapping the optimal solution(s) to a combinatorial optimization problem to the min…
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…
3SAT on an All-to-All-Connected CMOS Ising Solver Chip
Hüsrev Cılasun, Ziqing Zeng, Ramprasath S +6
This work solves 3SAT, a classical NP-complete problem, on a CMOS-based Ising hardware chip with all-to-all connectivity. The paper addresses practical issues in going from algorit…
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…
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…
SkyLogic - A proposal for a skyrmion logic device
Meghna G. Mankalale, Zhengyang Zhao, Jian-Ping Wang +1
This work proposes a novel logic device (SkyLogic) based on skyrmions, which are magnetic vortex-like structures that have low depinning current density and are robust to defects.…
CoMET: Composite-Input Magnetoelectric-based Logic Technology
Meghna G. Mankalale, Zhaoxin Liang, Zhengyang Zhao +3
This work proposes CoMET, a fast and energy-efficient spintronics device for logic applications. An input voltage is applied to a ferroelectric (FE) material, in contact with a com…
STEM: A Scheme for Two-phase Evaluation of Majority Logic
Meghna G. Mankalale, Zhaoxin Liang, Sachin S. Sapatnekar
The switching time of a magnet in a spin current based majority gate depends on the input vector combination, and this often restricts the speed of majority-based circuits. To addr…