5 papers
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Youngeun Kim, Hyoungseob Park, Abhishek Moitra +3
Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary s…
Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks
Abhiroop Bhattacharjee, Lakshya Bhatnagar, Priyadarshini Panda
Recently several structured pruning techniques have been introduced for energy-efficient implementation of Deep Neural Networks (DNNs) with lesser number of crossbars. Although, th…
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
With a growing need to enable intelligence in embedded devices in the Internet of Things (IoT) era, secure hardware implementation of Deep Neural Networks (DNNs) has become imperat…
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee +2
As neural networks gain widespread adoption in embedded devices, there is a need for model compression techniques to facilitate deployment in resource-constrained environments. Qua…
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…