RRAM-based multi-core systems improve the energy efficiency and performance of 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 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.

翻译：基于RRAM的多核系统提升了CNN的能效与性能。然而，卷积层的分布式并行执行会引发关键数据依赖，从而限制潜在加速比。本文提出了面向RRAM存算一体架构上卷积层并行推理的同步技术。我们提出了一种可实现高效数据交换的架构优化方案，并讨论了不同架构配置对性能的影响。相应的编译器算法针对CNN推理过程中的高加速比与低内存消耗进行了优化。针对当前主流的CNN基准测试，我们实现了超过99%的理论加速极限，且数据传输开销低于4%。