121 citations · 548 across the 26 of their papers we have counts for
27 papers
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…
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
Yuhang Li, Abhishek Moitra, Tamar Geller +1
Spiking Neural Networks (SNNs) have recently attracted widespread research interest as an efficient alternative to traditional Artificial Neural Networks (ANNs) because of their ca…
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…
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…
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…
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies
Li Siyao, Yuhang Li, Bo Li +3
Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations…
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…
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Bijie Bai, Yi Luo, Tianyi Gan +6
Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing…
Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration
Yuhang Li, Shikuang Deng, Xin Dong +1
Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtain…
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge
Kihyuk Hong, Yuhang Li, Ambuj Tewari
We propose an algorithm for non-stationary kernel bandits that does not require prior knowledge of the degree of non-stationarity. The algorithm follows randomized strategies obtai…
Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
Yuhang Li, Yi Luo, Bijie Bai +1
Imaging through diffusive media is a challenging problem, where the existing solutions heavily rely on digital computers to reconstruct distorted images. We provide a detailed anal…
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park +2
Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an e…
Neuromorphic Data Augmentation for Training Spiking Neural Networks
Yuhang Li, Youngeun Kim, Hyoungseob Park +2
Developing neuromorphic intelligence on event-based datasets with Spiking Neural Networks (SNNs) has recently attracted much research attention. However, the limited size of event-…
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Xiuying Wei, Ruihao Gong, Yuhang Li +2
Recently, post-training quantization (PTQ) has driven much attention to produce efficient neural networks without long-time retraining. Despite its low cost, current PTQ works tend…
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Shikuang Deng, Yuhang Li, Shanghang Zhang +1
Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics. Still, it is…
Neural Architecture Search for Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +2
Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-s…
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Yuhang Li, Mingzhu Shen, Jian Ma +6
Model quantization has emerged as an indispensable technique to accelerate deep learning inference. While researchers continue to push the frontier of quantization algorithms, exis…
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Yuhang Li, Shikuang Deng, Xin Dong +2
Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the…
Diversifying Sample Generation for Accurate Data-Free Quantization
Xiangguo Zhang, Haotong Qin, Yifu Ding +6
Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical…
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Yuhang Li, Ruihao Gong, Xu Tan +6
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training…