597 citations · 4.3k across the 121 of their papers we have counts for
23 papers · 1 filter
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…
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…
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks
Nitin Rathi, Kaushik Roy
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals (or spikes) distributed over time, can potentially lead to greater computational efficiency…
Towards Understanding the Effect of Leak in Spiking Neural Networks
Sayeed Shafayet Chowdhury, Chankyu Lee, Kaushik Roy
Spiking Neural Networks (SNNs) are being explored to emulate the astounding capabilities of human brain that can learn and compute functions robustly and efficiently with noisy spi…
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
Maryam Parsa, Catherine D. Schuman, Prasanna Date +8
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms…
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…
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…
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks
Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu +3
Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based camer…
Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation
Deboleena Roy, Priyadarshini Panda, Kaushik Roy
Spiking neural networks (SNNs) offer a promising alternative to current artificial neural networks to enable low-power event-driven neuromorphic hardware. Spike-based neuromorphic…
Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra Analysis
Priyadarshini Panda, Efstathia Soufleri, Kaushik Roy
We analyze the stability of recurrent networks, specifically, reservoir computing models during training by evaluating the eigenvalue spectra of the reservoir dynamics. To circumve…
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks
Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar +3
In this era of machine learning models, their functionality is being threatened by adversarial attacks. In the face of this struggle for making artificial neural networks robust, f…
Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures
Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda +2
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing c…
Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks
Jason M. Allred, Kaushik Roy
Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrol…
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition
Nitin Rathi, Priyadarshini Panda, Kaushik Roy
Spiking Neural Networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limit…
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy
Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networ…
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy
Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficie…
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
Priyadarshini Panda, Jason M. Allred, Shriram Ramanathan +1
A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adap…
Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnets
Akhilesh Jaiswal, Sourjya Roy, Gopalakrishnan Srinivasan +1
The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing. Hardware implementations…
Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing
Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan +1
Large-scale artificial neural networks have shown significant promise in addressing a wide range of classification and recognition applications. However, their large computational…
Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks
Gopalakrishnan Srinivasan, Parami Wijesinghe, Syed Shakib Sarwar +2
Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with…
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition
Priyadarshini Panda, Kaushik Roy
We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky inte…