A Virtual Prototype of Scalable Network-on-Chip Design

A Virtual Prototype of Network-on-Chip (NoC) that interconnects IPs in System-on-Chip is presented in this thesis. A Virtual Prototype is a software model describing various components of NoC put together for simulation and experiments of large SoCs (System-on-Chips). It is a practical way to validate interconnection and working of SoCs with a large number of components in scalable manner. In spite of extensive studies on NoC design, a virtual prototype of NoC is unavailable to academic community. The proposed cycle accurate model of NoC is perhaps the first academic virtual prototype of NoC (VPNoC). The VPNoC can provide similar functionalities as the NoC in the existing simulators. Furthermore, since it is implemented on Carbon SoC Designer, an ARM based SoC development tool, it can be applied directly to current/future SoC design. The proposed VPNoC has been used to demonstrate the design of two SoC applications. In this study, we have achieved: 1) designs and implementations of the NoC components and the VPNoC, 2) measurement of throughput and latency for the VPNoC, and 3) two data intensive applications and their performance analysis

High Performance Information Filtering on Many-core Processors

The increasing amount of information accessible to a user digitally makes search difficult, time consuming and unsatisfactory. This has led to the development of active information filtering (recommendation) systems that learn a user’s preference and filter out the most relevant information using sophisticated machine learning techniques. To be scalable and effective, such systems are currently deployed in cloud infrastructures consisting of general-purpose computers. The emergence of many-core processors as compute nodes in cloud infrastructures necessitates a revisit of the computational model, run-time, memory hierarchy and I/O pipelines to fully exploit available concurrency within these processors. This research proposes algorithms & architectures to enhance the performance of content-based (CB) and collaborative information filtering (CF) on many-core processors. To validate these methods, we use Nvidia’s Tesla, Fermi and Kepler GPUs and Intel’s experimental single chip cloud computer (SCC) as the target platforms. We observe that ~290x speedup and up to 97% energy savings over conventional sequential approaches. Finally, we propose and validate a novel reconfigurable SoC architecture which combines the best features of GPUs & SCC. This has been validated to show ~98K speedup over SCC and ~15K speedup over GPU