Futurists often speak of society’s inevitable technological “singularity,” a point in the near future where computers will become ubiquitous units, seamlessly integrated in everyday objects. This trend is already being foreshadowed by manycore processing via the network-on-chip approach, a novel paradigm which implements on-chip networks that enable platforms with extreme parallel capabilities. Our group seeks to develop new machine learning, optimization, and resource management techniques which can enable such a fundamental shift for energy-efficient, cost-effective, large-scale distributed computational platforms for both embedded and high-performance applications.
CANNON: Communication-Aware Sparse Neural Network Optimization Journal Article Forthcoming
In: IEEE Transactions on Emerging Topics in Computing, Forthcoming.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
In: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1-9, 2022.
Model Elasticity for Hardware Heterogeneity in Federated Learning Systems Proceedings Article
In: Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (FedEdge), pp. 19-24, 2022.
In: ACM Transactions on Embedded Computing Systems (TECS), 2022.
In: The Conference on Robot Learning, 2021.