49 citations · 169 across the 9 of their papers we have counts for
9 papers
Hierarchical Weight Averaging for Deep Neural Networks
Xiaozhe Gu, Zixun Zhang, Yuncheng Jiang +4
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs). Among various attempts to improve SGD, weight aver…
A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding
Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu +2
Spiking neural networks (SNNs) recently gained momentum due to their low-power multiplication-free computing and the closer resemblance of biological processes in the nervous syste…
Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases
Tian Huang, Jun Xu, Tao Luo +3
Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantu…
E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs
Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu +2
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. They allow researchers and developers, who are not familiar with hardware engineeri…
Efficient Spiking Neural Networks with Radix Encoding
Zhehui Wang, Xiaozhe Gu, Rick Goh +2
Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) due to its event-driven computation mechanism and…
Resource Efficient Isolation Mechanisms in Mixed-Criticality Scheduling
Xiaozhe Gu, Arvind Easwaran, Kieu-My Phan +1
Mixed-criticality real-time scheduling has been developed to improve resource utilization while guaranteeing safe execution of critical applications. These studies use optimistic r…
Dynamic Budget Management with Service Guarantees for Mixed-Criticality Systems
Xiaozhe Gu, Arvind Easwaran
Many existing studies on mixed-criticality (MC) scheduling assume that low-criticality budgets for high-criticality applications are known apriori. These budgets are primarily used…
Efficient Schedulability Test for Dynamic-Priority Scheduling of Mixed-Criticality Real-Time Systems
Xiaozhe Gu, Arvind Easwaran
Systems in many safety-critical application domains are subject to certification requirements. In such a system, there are typically different applications providing functionalitie…
Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
Xiaozhe Gu, Arvind Easwaran
Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical Systems among which many are safety-critical, e.g., chemical pla…