activity
20192021
most citedX-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks

21 citations · 22 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures

Chankyu Lee, Adarsh Kumar Kosta, Kaushik Roy

Standard frame-based cameras that sample light intensity frames are heavily impacted by motion blur for high-speed motion and fail to perceive scene accurately when the dynamic ran…

cs.NE2020

Towards Understanding the Effect of Leak in Spiking Neural Networks

Sayeed Shafayet Chowdhury, Chankyu Lee, Kaushik Roy

Spiking Neural Networks (SNNs) are being explored to emulate the astounding capabilities of human brain that can learn and compute functions robustly and efficiently with noisy spi…

cs.NE2020

Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks

Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu +3

Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based camer…

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…

cs.NE2019

A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks

Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar +3

In this era of machine learning models, their functionality is being threatened by adversarial attacks. In the face of this struggle for making artificial neural networks robust, f…

cs.NE2019

Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures

Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda +2

Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing c…