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.
INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip Inproceedings Forthcoming
In: 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Forthcoming.
Model Elasticity for Hardware Heterogeneity in Federated Learning Systems Inproceedings Forthcoming
In: FedEdge 2022 - 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network, Forthcoming.
Domain-Specific Architectures (DSAs): Research Problems and Promising Approaches Journal Article Forthcoming
In: ACM Transactions on Embedded Computing Systems, Forthcoming.
In: The Conference on Robot Learning, 2021.
In: IEEE Embedded Systems Letters, 2021.
In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 4064-4077, 2020.