Modeling and Mapping of a GoogLeNet CNN on a Grid of Processing Cells

System-level design methodologies evolve in response to increasing complexity of applications. Transaction-level modeling (TLM) is one technique that allows the designer to capture the specifications of complex digital systems without defining low-level implementation details. The Grid of Processing Cells (GPC) has been proposed as a highly scalable many-core architecture and is modeled using SystemC TLM-2.0 methodology. This thesis describes the modeling of a GoogLeNet Convolutional Neural Network (CNN) on the GPC architecture and evaluates its performance and scalability. The models feature a new modular Memory Access Resources and Interfaces (MARI) library to improve communication between modules and assist during profiling. This work also introduces a graphical CAD software called Map Grid-based Layouts (MapGL) to facilitate the design process, automatically generate SystemC models, and generate performance reports. Experimental results evaluate and compare the generated models and show the achieved improvements in terms of memory usage and speed.