Zobrazeno 1 - 10
of 85
pro vyhledávání: '"He, Zhezhi"'
Spiking neural networks (SNNs) are becoming a promising alternative to conventional artificial neural networks (ANNs) due to their rich neural dynamics and the implementation of energy-efficient neuromorphic chips. However, the non-differential binar
Externí odkaz:
http://arxiv.org/abs/2408.09403
Autor:
Wu, Hongqiu, Xu, Zekai, Xu, Tianyang, Wei, Shize, Wang, Yan, Hong, Jiale, Wu, Weiqi, Zhao, Hai, Zhang, Min, He, Zhezhi
In this paper, we focus on the \emph{virtual world}, a cyberspace where people can live in. An ideal virtual world shares great similarity with our real world. One of the crucial aspects is its evolving nature, reflected by individuals' capability to
Externí odkaz:
http://arxiv.org/abs/2408.05842
Publikováno v:
European Conference on Computer Vision 2024
Spiking neural networks (SNNs), which mimic biological neural system to convey information via discrete spikes, are well known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete s
Externí odkaz:
http://arxiv.org/abs/2407.09083
Publikováno v:
International Conference on Machine Learning 2024
Spiking neural network (SNN) has attracted great attention due to its characteristic of high efficiency and accuracy. Currently, the ANN-to-SNN conversion methods can obtain ANN on-par accuracy SNN with ultra-low latency (8 time-steps) in CNN structu
Externí odkaz:
http://arxiv.org/abs/2406.03470
Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation. However, in practice, it achieves little
Externí odkaz:
http://arxiv.org/abs/2403.00835
Autor:
Li, Jingtao, Rakin, Adnan Siraj, Chen, Xing, Yang, Li, He, Zhezhi, Fan, Deliang, Chakrabarti, Chaitali
Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulnerable to Intellectual Property (IP) threats. Since FL periodically coll
Externí odkaz:
http://arxiv.org/abs/2303.08581
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. Wh
Externí odkaz:
http://arxiv.org/abs/2205.04007
Vision transformer (ViT) has achieved competitive accuracy on a variety of computer vision applications, but its computational cost impedes the deployment on resource-limited mobile devices. We explore the sparsity in ViT and observe that informative
Externí odkaz:
http://arxiv.org/abs/2203.04570
Accelerating the neural network inference by FPGA has emerged as a popular option, since the reconfigurability and high performance computing capability of FPGA intrinsically satisfies the computation demand of the fast-evolving neural algorithms. Ho
Externí odkaz:
http://arxiv.org/abs/2112.08193
Neural network stealing attacks have posed grave threats to neural network model deployment. Such attacks can be launched by extracting neural architecture information, such as layer sequence and dimension parameters, through leaky side-channels. To
Externí odkaz:
http://arxiv.org/abs/2107.09789