most citedNode-Centric Graph Learning from Data for Brain State Identification

11 citations · 27 across the 7 of their papers we have counts for

collaborators

8 papers

eess.SY202211 cited

A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter with Simultaneous Inductive Powering and Data Receiving

Nima Soltani, Hamed M. Jafari, Karim Abdelhalim +3

An ultra-wide-band impulse-radio (UWB-IR) transmitter (TX) for low-energy biomedical microsystems is presented. High power efficiency is achieved by modulating an LC tank that alwa…

cs.AR20221 cited

PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators

Ali Alshaarawy, Amirali Amirsoleimani, Roman Genov

In this work, PRUNIX, a framework for training and pruning convolutional neural networks is proposed for deployment on memristor crossbar based accelerators. PRUNIX takes into acco…

cs.ET20222 cited

SDEX: Monte Carlo Simulation of Stochastic Differential Equations on Memristor Crossbars

Louis Primeau, Amirali Amirsoleimani, Roman Genov

Here we present stochastic differential equations (SDEs) on a memristor crossbar, where the source of gaussian noise is derived from the random conductance due to ion drift in the…

cs.CR20221 cited

HYPERLOCK: In-Memory Hyperdimensional Encryption in Memristor Crossbar Array

Jack Cai, Amirali Amirsoleimani, Roman Genov

We present a novel cryptography architecture based on memristor crossbar array, binary hypervectors, and neural network. Utilizing the stochastic and unclonable nature of memristor…

cs.ET2022

Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures

Corey Lammie, Jason K. Eshraghian, Chenqi Li +4

The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL…

cs.LG202011 cited

Node-Centric Graph Learning from Data for Brain State Identification

Nafiseh Ghoroghchian, David M. Groppe, Roman Genov +2

Data-driven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph…