8 papers
Learning Temporal Causal Structure via Smooth Differentiable Optimization
Tong Zhao, Ce Guo, Wayne Luk +2
Causal discovery with instantaneous effects in multivariate time series is challenging, as the instantaneous structure must be acyclic. Prior methods enforce this by either separat…
SPAC: Automating FPGA-based Network Switches with Protocol Adaptive Customization
Guoyu Li, Yang Cao, Lucas H L Ng +9
With network requirements diverging across emerging applications, latency-critical services demand minimal logic delay, while hyperscale training and collectives require sustained…
Robust Time Series Causal Discovery for Agent-Based Model Validation
Gene Yu, Ce Guo, Wayne Luk
Agent-Based Model (ABM) validation is crucial as it helps ensuring the reliability of simulations, and causal discovery has become a powerful tool in this context. However, current…
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…
Scalable Time-Series Causal Discovery with Approximate Causal Ordering
Ziyang Jiao, Ce Guo, Wayne Luk
Causal discovery in time-series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or sampl…
ASPO: Constraint-Aware Bayesian Optimization for FPGA-based Soft Processors
Haoran Wu, Ce Guo, Wayne Luk +1
Bayesian Optimization (BO) has shown promise in tuning processor design parameters. However, standard BO does not support constraints involving categorical parameters such as types…