440 citations · 622 across the 13 of their papers we have counts for
14 papers
The Easy Path to Robustness: Coreset Selection using Sample Hardness
Pranav Ramesh, Arjun Roy, Deepak Ravikumar +2
Designing adversarially robust models from a data-centric perspective requires understanding which input samples are most crucial for learning resilient features. While coreset sel…
Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems
Yinghan Long, Indranil Chakraborty, Gopalakrishnan Srinivasan +1
The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data process…
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda +1
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations…
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
Bing Han, Gopalakrishnan Srinivasan, Kaushik Roy
Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven da…
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan +1
Modern deep networks have millions to billions of parameters, which leads to high memory and energy requirements during training as well as during inference on resource-constrained…
Explicitly Trained Spiking Sparsity in Spiking Neural Networks with Backpropagation
Jason M. Allred, Steven J. Spencer, Gopalakrishnan Srinivasan +1
Spiking Neural Networks (SNNs) are being explored for their potential energy efficiency resulting from sparse, event-driven computations. Many recent works have demonstrated effect…