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20152025
most citedSeizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

43 citations · 56 across the 9 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.ET2022★ 43 cited

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

Chenqi Li, Corey Lammie, Xuening Dong +3

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware imple…

cs.LG2022★ 2 cited

Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation

Tim Zhang, Corey Lammie, Mostafa Rahimi Azghadi +3

Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementin…

cs.AR2022★ 1 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.ET2022★ 2 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.CR2022★ 1 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…