11 citations · 14 across the 3 of their papers we have counts for
8 papers
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Ling Liang, Xing Hu, Lei Deng +5
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models ac…
Towards Efficient Superconducting Quantum Processor Architecture Design
Gushu Li, Yufei Ding, Yuan Xie
More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more comp…
Proq: Projection-based Runtime Assertions for Debugging on a Quantum Computer
Gushu Li, Li Zhou, Nengkun Yu +3
In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer. The predicates in Proq are represented by projections (…
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Lei Deng, Yujie Wu, Yifan Hu +6
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with h…
AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms
Yuke Wang, Boyuan Feng, Gushu Li +3
As a promising solution to boost the performance of distance-related algorithms (e.g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with num…
KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA
Yuke Wang, Zhaorui Zeng, Boyuan Feng +2
K-means is a popular but computation-intensive algorithm for unsupervised learning. To address this issue, we present KPynq, a work-efficient triangle-inequality based K-means on F…