79 citations · 495 across the 38 of their papers we have counts for
13 papers · 1 filter
A Low Effort Approach to Structured CNN Design Using PCA
Isha Garg, Priyadarshini Panda, Kaushik Roy
Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods that often result in overparametrized ne…
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
Shubham Jain, Abhronil Sengupta, Kaushik Roy +1
Resistive crossbars designed with non-volatile memory devices have emerged as promising building blocks for Deep Neural Network (DNN) hardware, due to their ability to compactly an…
A Photonic In-Memory Computing primitive for Spiking Neural Networks using Phase-Change Materials
Indranil Chakraborty, Gobinda Saha, Kaushik Roy
Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This…
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness
Priyadarshini Panda, Kaushik Roy
We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative mod…
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
Amogh Agrawal, Akhilesh Jaiswal, Deboleena Roy +4
Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognitio…
Exploiting Inherent Error-Resiliency of Neuromorphic Computing to achieve Extreme Energy-Efficiency through Mixed-Signal Neurons
Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity +3
Neuromorphic computing, inspired by the brain, promises extreme efficiency for certain classes of learning tasks, such as classification and pattern recognition. The performance an…