440 citations · 1.2k across the 89 of their papers we have counts for
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NEAT: Non-linearity Aware Training for Accurate and Energy-Efficient Implementation of Neural Networks on 1T-1R Memristive Crossbars
Abhiroop Bhattacharjee, Lakshya Bhatnagar, Youngeun Kim +1
Memristive crossbars suffer from non-idealities (such as, sneak paths) that degrade computational accuracy of the Deep Neural Networks (DNNs) mapped onto them. A 1T-1R synapse, add…
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks
Abhishek Moitra, Priyadarshini Panda
Deep Learning is able to solve a plethora of once impossible problems. However, they are vulnerable to input adversarial attacks preventing them from being autonomously deployed in…
SwitchX: Gmin-Gmax Switching for Energy-Efficient and Robust Implementation of Binary Neural Networks on ReRAM Xbars
Abhiroop Bhattacharjee, Priyadarshini Panda
Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of th…
Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch
Youngeun Kim, Priyadarshini Panda
Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield…
Compression-aware Continual Learning using Singular Value Decomposition
Varigonda Pavan Teja, Priyadarshini Panda
We propose a compression based continual task learning method that can dynamically grow a neural network. Inspired from the recent model compression techniques, we employ compressi…
Large Associative Memory Problem in Neurobiology and Machine Learning
Dmitry Krotov, John Hopfield
Dense Associative Memories or modern Hopfield networks permit storage and reliable retrieval of an exponentially large (in the dimension of feature space) number of memories. At th…