440 citations · 1.2k across the 89 of their papers we have counts for
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Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation
Youngeun Kim, Joshua Chough, Priyadarshini Panda
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) because of their sparse, asynchronous, and binary event-drive…
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…
DetectX -- Adversarial Input Detection using Current Signatures in Memristive XBar Arrays
Abhishek Moitra, Priyadarshini Panda
Adversarial input detection has emerged as a prominent technique to harden Deep Neural Networks(DNNs) against adversarial attacks. Most prior works use neural network-based detecto…
Federated Learning with Spiking Neural Networks
Yeshwanth Venkatesha, Youngeun Kim, Leandros Tassiulas +1
As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emergin…
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…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…