597 citations · 4.3k across the 121 of their papers we have counts for
129 papers
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks
Marco Paul E. Apolinario, Adarsh Kumar Kosta, Utkarsh Saxena +1
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge de…
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Wilfried Haensch, Anand Raghunathan, Kaushik Roy +4
Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of tod…
Cryogenic Neuromorphic Hardware
Md Mazharul Islam, Shamiul Alam, Md Shafayat Hossain +2
The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main cha…
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing
Bing Han, Cheng Wang, Kaushik Roy
Tremendous progress has been made in sequential processing with the recent advances in recurrent neural networks. However, recurrent architectures face the challenge of exploding/v…
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Sayeed Shafayet Chowdhury, Nitin Rathi, Kaushik Roy
Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly used deep neural networks (DNNs). Through event-driven information processing, SNNs can reduce the expe…
Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning
Wachirawit Ponghiran, Kaushik Roy
Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons, can be operated in an event-driven manner and have internal states to retain information over time, prov…