79 citations · 235 across the 32 of their papers we have counts for
10 papers · 1 filter
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning
Adarsh Kumar Kosta, Malik Aqeel Anwar, Priyadarshini Panda +2
Present-day Deep Reinforcement Learning (RL) systems show great promise towards building intelligent agents surpassing human-level performance. However, the computational complexit…
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…
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…
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks
Priyadarshini Panda
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial attacks, wherein, a model gets fooled by applying slight perturbations on the input. With the advent of…
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…