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
Modeling, Analysis and Design of Bio-hybrid Micro-robotic Swarms for Medical Applications Book Section
In: Modeling, Methodologies and Tools for Molecular and Nano-scale Communications, pp. 517–539, Springer, Cham, 2017.
Imitation learning for dynamic VFI control in large-scale manycore systems Journal Article
In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, no. 9, pp. 2458–2471, 2017.
3D NoC-enabled heterogeneous manycore architectures for accelerating CNN training: performance and thermal trade-offs Proceedings Article
In: 2017 Eleventh IEEE/ACM International Symposium on Networks-on-Chip (NOCS), pp. 1–8, IEEE 2017.
On-chip communication network for efficient training of deep convolutional networks on heterogeneous manycore systems Journal Article
In: IEEE Transactions on Computers, vol. 67, no. 5, pp. 672–686, 2017.
Hybrid network-on-chip architectures for accelerating deep learning kernels on heterogeneous manycore platforms Proceedings Article
In: Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems, pp. 1–10, 2016.
nOS: A nano-sized distributed operating system for many-core embedded systems Proceedings Article
In: 2016 IEEE 34th International Conference on Computer Design (ICCD), pp. 177–184, IEEE 2016.
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.