Mapping of CNNs on multi-core RRAM-based CIM architectures

Resistive random access memory (RRAM)-based multi-core systems improve the energy efficiency and performance of convolutional neural networks (CNNs). Thereby, the distributed parallel execution of convolutional layers causes critical data dependencies that limit the potential speedup. This paper presents synchronization techniques for parallel inference of convolutional layers on RRAM-based computing-in-memory (CIM) architectures. We propose an architecture optimization that enables efficient data exchange and discuss the impact of different architecture setups on the performance. The corresponding compiler algorithms are optimized for high speedup and low memory consumption during CNN inference. We achieve more than 99% of the theoretical acceleration limit with a marginal data transmission overhead of less than 4% for state-of-the-art CNN benchmarks.