597 citations · 4.3k across the 121 of their papers we have counts for
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Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures
Chankyu Lee, Adarsh Kumar Kosta, Kaushik Roy
Standard frame-based cameras that sample light intensity frames are heavily impacted by motion blur for high-speed motion and fail to perceive scene accurately when the dynamic ran…
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations
Saima Sharmin, Nitin Rathi, Priyadarshini Panda +1
In the recent quest for trustworthy neural networks, we present Spiking Neural Network (SNN) as a potential candidate for inherent robustness against adversarial attacks. In this w…
Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization
Priyadarshini Panda, Aparna Aketi, Kaushik Roy
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on…
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing
Gopalakrishnan Srinivasan, Kaushik Roy
In this work, we propose ReStoCNet, a residual stochastic multilayer convolutional Spiking Neural Network (SNN) composed of binary kernels, to reduce the synaptic memory footprint…
A Low Effort Approach to Structured CNN Design Using PCA
Isha Garg, Priyadarshini Panda, Kaushik Roy
Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods that often result in overparametrized ne…
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning
Deboleena Roy, Priyadarshini Panda, Kaushik Roy
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and…
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Abhronil Sengupta, Yuting Ye, Robert Wang +2
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application…
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
Syed Shakib Sarwar, Aayush Ankit, Kaushik Roy
Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to…
FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition
Priyadarshini Panda, Aayush Ankit, Parami Wijesinghe +1
Machine-learning algorithms have shown outstanding image recognition or classification performance for computer vision applications. However, the compute and energy requirement for…
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition
Priyadarshini Panda, Kaushik Roy
One of the key challenges in machine learning is to design a computationally efficient multi-class classifier while maintaining the output accuracy and performance. In this paper,…
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
Priyadarshini Panda, Abhronil Sengupta, Kaushik Roy
Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy requirements asso…
Energy-Efficient Object Detection using Semantic Decomposition
Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta +2
Machine-learning algorithms offer immense possibilities in the development of several cognitive applications. In fact, large scale machine-learning classifiers now represent the st…