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20122021
most citedGabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks

79 citations · 495 across the 38 of their papers we have counts for

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21 papers · 1 filter

cs.ET20217 cited

NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems

Shubham Negi, Indranil Chakraborty, Aayush Ankit +1

In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs) are gaining popularity to accelerate Deep Neural Networks (DNNs) since it alleviates the "memory wall" pr…

cs.ET2020

On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks

Deboleena Roy, Indranil Chakraborty, Timur Ibrayev +1

The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such…

cs.ET2020

IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array

Mustafa Ali, Akhilesh Jaiswal, Sangamesh Kodge +3

`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck. This emerging paradigm aims at embedding some aspects of…

cs.ET2020

GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks

Indranil Chakraborty, Mustafa Fayez Ali, Dong Eun Kim +2

The analog nature of computing in Memristive crossbars poses significant issues due to various non-idealities such as: parasitic resistances, non-linear I-V characteristics of the…

cs.ET2020

sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge

Minsuk Koo, Gopalakrishnan Srinivasan, Yong Shim +1

In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes…

cs.ET201921 cited

X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks

Amogh Agrawal, Chankyu Lee, Kaushik Roy

There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and…