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
Adaptive models for input data compaction for power simulators Proceedings Article
In: Proceedings of ASP-DAC'97: Asia and South Pacific Design Automation Conference, pp. 391–396, IEEE 1997.
Hierarchical sequence compaction for power estimation Proceedings Article
In: Proceedings of the 34th annual Design Automation Conference, pp. 570–575, 1997.
Sequence compaction for probabilistic analysis of finite-state machines Proceedings Article
In: Proceedings of the 34th annual Design Automation Conference, pp. 12–15, 1997.
Composite sequence compaction for finite-state machines using block entropy and high-order Markov models Proceedings Article
In: Proceedings of 1997 International Symposium on Low Power Electronics and Design, pp. 190–195, IEEE 1997.
Vector compaction using dynamic Markov models Journal Article
In: IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, vol. 80, no. 10, pp. 1924–1933, 1997.
Steady-State Probability Estimation in FSM’s Considering High-Order Temporal Effects,’ Journal Article
In: Technical Report, 1997.
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.