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.
Selected Publications
DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs Journal Article
In: IEEE Embedded Systems Letters, 2021.
Runtime Task Scheduling Using Imitation Learning for Heterogeneous Many-Core Systems Journal Article
In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 4064-4077, 2020.
DS3: A system-level domain-specific system-on-chip simulation framework Journal Article
In: IEEE Transactions on Computers, 2020.
HiLITE: Hierarchical and Lightweight Imitation Learning for Power Management of Embedded SoCs Journal Article
In: IEEE Computer Architecture Letters, vol. 19, no. 1, pp. 63–67, 2020.
Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-Core Systems Journal Article
In: arXiv preprint arXiv:2007.09361, 2020.
Edge AI: Systems Design and ML for IoT Data Analytics Proceedings Article
In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3565–3566, 2020.
Embedded Systems
Embedded systems are computer systems that perform dedicated functions while being parts of a larger system. Our research targets primarily heterogeneous many-core System on a Chip (SoC) platforms where communication happens via the network-on-chip. These SoCs should be designed to meet aggressive performance requirements, while coping with limited battery capacity, thermal design power, and real-time constraints. Over the years, we have considered deterministic, probabilistic, and statistical physics-inspired design paradigms. Lately, our research targets machine learning approaches (e.g., imitation and reinforcement learning) for performance and energy optimization and resource management in heterogeneous SoC platforms.
Cyber-Physical Systems
Cyber-physical systems (CPS) refer to a new generation of networked embedded systems that bring together sensing, computation, communication, control and actuation in order to sustain a continuous interaction with the physical world (e.g., processes taking place on electrical power grids, transportation and traffic roads, communication and financial networks, medical devices, smart buildings, etc.). Physical processes are predominantly non-stationary in nature and require time-dependent models for understanding their behavior. Our research focuses on accurate modeling physical processes to better understand the theoretical foundations of CPS design and optimization.