Near-Memory Computing Compiler for Neural Network Architectures
With an increased popularity of machine learning, both higher performance and more energy-efficient circuits are needed to meet the demands of increasing workloads. This master's thesis focuses on convolutional neural networks and implements a compiler that generates an accelerator architecture that can be tailored to performance needs. The implemented architecture utilizes near-memory computi
