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09/06/2023
Are SNNs Truly Energy-efficient? - A Hardware Perspective
Spiking Neural Networks (SNNs) have gained attention for their energy-ef...
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08/31/2023
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
We introduce a method to convert Physics-Informed Neural Networks (PINNs...
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05/27/2023
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
Spiking Neural Networks (SNNs) have recently attracted widespread resear...
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05/26/2023
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained increasing attention as energ...
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05/26/2023
Do We Really Need a Large Number of Visual Prompts?
Due to increasing interest in adapting models on resource-constrained ed...
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05/16/2023
MINT: Multiplier-less Integer Quantization for Spiking Neural Networks
We propose Multiplier-less INTeger (MINT) quantization, an efficient uni...
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05/11/2023
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
Federated Learning (FL) is a privacy-preserving distributed machine lear...
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04/25/2023
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
Spiking Neural Networks (SNNs) are recognized as the candidate for the n...
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04/02/2023
SEENN: Towards Temporal Spiking Early-Exit Neural Networks
Spiking Neural Networks (SNNs) have recently become more popular as a bi...
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03/30/2023
XPert: Peripheral Circuit Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing
The hardware-efficiency and accuracy of Deep Neural Networks (DNNs) impl...
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02/15/2023
XploreNAS: Explore Adversarially Robust Hardware-efficient Neural Architectures for Non-ideal Xbars
Compute In-Memory platforms such as memristive crossbars are gaining foc...
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02/13/2023
Workload-Balanced Pruning for Sparse Spiking Neural Networks
Pruning for Spiking Neural Networks (SNNs) has emerged as a fundamental ...
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02/09/2023
DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks
With ever increasing depth and width in deep neural networks to achieve ...
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11/26/2022
Exploring Temporal Information Dynamics in Spiking Neural Networks
Most existing Spiking Neural Network (SNN) works state that SNNs may uti...
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11/14/2022
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
We study the Human Activity Recognition (HAR) task, which predicts user ...
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10/24/2022
SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks
SNNs are an active research domain towards energy efficient machine inte...
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07/04/2022
Lottery Ticket Hypothesis for Spiking Neural Networks
Spiking Neural Networks (SNNs) have recently emerged as a new generation...
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06/20/2022
Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
Spiking Neural Networks (SNNs) have recently emerged as the low-power al...
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04/11/2022
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained huge attention as a potential...
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04/11/2022
MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning
Recent years have seen a paradigm shift towards multi-task learning. Thi...
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03/24/2022
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
Federated learning has been extensively studied and is the prevalent met...
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03/11/2022
Neuromorphic Data Augmentation for Training Spiking Neural Networks
Developing neuromorphic intelligence on event-based datasets with spikin...
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02/09/2022
Adversarial Detection without Model Information
Most prior state-of-the-art adversarial detection works assume that the ...
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01/31/2022
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Recent Spiking Neural Networks (SNNs) works focus on an image classifica...
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01/23/2022
Neural Architecture Search for Spiking Neural Networks
Spiking Neural Networks (SNNs) have gained huge attention as a potential...
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01/13/2022
Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks
Recently several structured pruning techniques have been introduced for ...
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01/05/2022
Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar
Binary memristive crossbars have gained huge attention as an energy-effi...
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10/14/2021
Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation
Spiking Neural Networks (SNNs) have recently emerged as the low-power al...
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09/16/2021
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning
Present-day Deep Reinforcement Learning (RL) systems show great promise ...
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06/22/2021
DetectX – Adversarial Input Detection using Current Signatures in Memristive XBar Arrays
Adversarial input detection has emerged as a prominent technique to hard...
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06/11/2021
Federated Learning with Spiking Neural Networks
As neural networks get widespread adoption in resource-constrained embed...
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05/09/2021
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks
With a growing need to enable intelligence in embedded devices in the In...
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04/07/2021
PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks
How can we bring both privacy and energy-efficiency to a neural system o...
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03/26/2021
Visual Explanations from Spiking Neural Networks using Interspike Intervals
Spiking Neural Networks (SNNs) compute and communicate with asynchronous...
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01/12/2021
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
As neural networks gain widespread adoption in embedded devices, there i...
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01/05/2021
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks
Neural networks have achieved remarkable performance in computer vision,...
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11/26/2020
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks
Deep Learning is able to solve a plethora of once impossible problems. H...
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10/05/2020
Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch
Spiking Neural Networks (SNNs) have recently emerged as an alternative t...
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09/03/2020
Compression-aware Continual Learning using Singular Value Decomposition
We propose a compression based continual task learning method that can d...
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08/25/2020
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks
Deep Neural Networks (DNNs) have been shown to be prone to adversarial a...
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07/03/2020
Domain Adaptation without Source Data
Domain adaptation assumes that samples from source and target domains ar...
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05/04/2020
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events...
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04/22/2020
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversar...
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03/23/2020
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations
In the recent quest for trustworthy neural networks, we present Spiking ...
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03/05/2020
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Modern deep networks have millions to billions of parameters, which lead...
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03/02/2020
Energy-efficient and Robust Cumulative Training with Net2Net Transformation
Deep learning has achieved state-of-the-art accuracies on several comput...
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02/25/2020
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors
Deep neural networks have demonstrated state-of-the-art performance on m...
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02/07/2020
Activation Density driven Energy-Efficient Pruning in Training
The process of neural network pruning with suitable fine-tuning and retr...
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10/30/2019
Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative...
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05/24/2019
