22 citations · 53 across the 25 of their papers we have counts for
7 papers · 1 filter
Coset Ensemble Decoder for Quantum Error Correction with Algorithm-Hardware Co-Design
Shuang Liang, Jubo Xu, Giulio Bassanino +8
Reliable large-scale quantum computation relies on fault-tolerant architectures, where quantum error correction (QEC) continuously extracts and decodes error syndromes in real time…
Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
Haoran Wu, Can Xiao, Jiayi Nie +15
LLMs now form the backbone of AI agents across a diverse range of applications, including tool use, command-line interfaces, and web or computer interaction. These agentic LLM infe…
MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration
Zhiqiang Que, Jose G. F. Coutinho, Ce Guo +2
This paper presents a unified framework for codifying and automating optimization strategies to efficiently deploy deep neural networks (DNNs) on resource-constrained hardware, suc…
Accelerating MRI Uncertainty Estimation with Mask-based Bayesian Neural Network
Zehuan Zhang, Matej Genci, Hongxiang Fan +2
Accurate and reliable Magnetic Resonance Imaging (MRI) analysis is particularly important for adaptive radiotherapy, a recent medical advance capable of improving cancer diagnosis…
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
Hongxiang Fan, Thomas Chau, Stylianos I. Venieris +5
Attention-based neural networks have become pervasive in many AI tasks. Despite their excellent algorithmic performance, the use of the attention mechanism and feed-forward network…
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
Zhiqiang Que, Hongxiang Fan, Marcus Loo +5
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performanc…
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Hongxiang Fan, Martin Ferianc, Miguel Rodrigues +3
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs…