Entries by Academic Web Pages


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.

Worm_Sim: a cycle accurate simulator for Networks-on-Chip

Worm_sim is a cycle-accurate simulator which we developed from scratch in C++ using standard template library. Worm_sim is written with flexibility and modularity in mind. It can be used to simulate a wide range of NoC architectures (e.g. NoCs with different topologies and different routing algorithms, etc.), using user controllable performance parameters (e.g. channel buffer size, routing engine delay, crossbar arbitration delay, etc.). Moreover, due to the flexibility of worm_sim, it can be easily extended to simulate NoCs which worm_sim does not support at the current stage.

BNSim: bacteria network simulator

Bacteria-based networks are formed using native or engineered bacteria that communicate at nano-scale. This definition includes the micro-scale molecular transportation system which uses chemotactic bacteria for targeted cargo delivery, as well as genetic circuits for intercellular interactions like quorum sensing or light communication. To characterize the dynamics of bacterial networks accurately, we introduce BNSim, an opensource, parallel, stochastic, and multiscale modeling platform which integrates various simulation algorithms, together with genetic circuits and chemotactic pathway models in a complex 3D environment.