88 citations · 255 across the 45 of their papers we have counts for
47 papers
MD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural Network
Donghyun Lee, Abhishek Moitra, Youngeun Kim +2
Spiking Neural Networks (SNNs) offer a promising and energy-efficient alternative to conventional neural networks, thanks to their sparse binary activation. However, they face chal…
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
Shiting Xiao, Yuhang Li, Youngeun Kim +2
Spiking Neural Networks (SNNs) have emerged as a compelling, energy-efficient alternative to traditional Artificial Neural Networks (ANNs) for static image tasks such as image clas…
Spiking Transformer with Spatial-Temporal Attention
Donghyun Lee, Yuhang Li, Youngeun Kim +2
Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results throu…
LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks
Ruokai Yin, Youngeun Kim, Di Wu +1
Spiking Neural Networks (SNNs) have gained significant research attention in the last decade due to their potential to drive resource-constrained edge devices. Though existing SNN…
When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design
Abhishek Moitra, Abhiroop Bhattacharjee, Yuhang Li +2
This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highl…
TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
Due to the high computation overhead of Vision Transformers (ViTs), In-memory Computing architectures are being researched towards energy-efficient deployment in edge-computing sce…
One-stage Prompt-based Continual Learning
Youngeun Kim, Yuhang Li, Priyadarshini Panda
Prompt-based Continual Learning (PCL) has gained considerable attention as a promising continual learning solution as it achieves state-of-the-art performance while preventing priv…
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Donghyun Lee, Ruokai Yin, Youngeun Kim +3
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation. How…
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
Yuhang Li, Youngeun Kim, Donghyun Lee +2
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency. However, it often hinges on the availabilit…
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
Youngeun Kim, Adar Kahana, Ruokai Yin +4
Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neuron…
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
In practical cloud-edge scenarios, where a resource constrained edge performs data acquisition and a cloud system (having sufficient resources) performs inference tasks with a deep…
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
Qian Zhang, Chenxi Wu, Adar Kahana +4
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to ha…
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
Abhiroop Bhattacharjee, Abhishek Moitra, Youngeun Kim +2
In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & co…
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Youngeun Kim, Yuhang Li, Abhishek Moitra +2
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation. However, their non-linear ac…
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park +1
Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural a…
Do We Really Need a Large Number of Visual Prompts?
Youngeun Kim, Yuhang Li, Abhishek Moitra +2
Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored. Among various methods, Visual Prompt Tu…
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
Yuhang Li, Youngeun Kim, Hyoungseob Park +1
Spiking Neural Networks (SNNs) are recognized as the candidate for the next-generation neural networks due to their bio-plausibility and energy efficiency. Recently, researchers ha…
SEENN: Towards Temporal Spiking Early-Exit Neural Networks
Yuhang Li, Tamar Geller, Youngeun Kim +1
Spiking Neural Networks (SNNs) have recently become more popular as a biologically plausible substitute for traditional Artificial Neural Networks (ANNs). SNNs are cost-efficient a…
XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
The hardware-efficiency and accuracy of Deep Neural Networks (DNNs) implemented on In-memory Computing (IMC) architectures primarily depend on the DNN architecture and the peripher…
Workload-Balanced Pruning for Sparse Spiking Neural Networks
Ruokai Yin, Youngeun Kim, Yuhang Li +4
Pruning for Spiking Neural Networks (SNNs) has emerged as a fundamental methodology for deploying deep SNNs on resource-constrained edge devices. Though the existing pruning method…
Exploring Temporal Information Dynamics in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +3
Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics…
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
Yuhang Li, Ruokai Yin, Hyoungseob Park +2
We study the Human Activity Recognition (HAR) task, which predicts user daily activity based on time series data from wearable sensors. Recently, researchers use end-to-end Artific…
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +3
Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks, which is suitable to be implemented on low-power mobile/edge devices. As…
Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
Abhiroop Bhattacharjee, Youngeun Kim, Abhishek Moitra +1
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary information p…