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.