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的多核系统提升了卷积神经网络（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关系，从而限制潜在加速比。本文提出面向RRAM存算一体架构的卷积层并行推理同步技术。我们通过架构优化实现高效数据交换，并探讨不同架构配置对性能的影响。相应编译器算法针对CNN推理过程中的高加速比与低内存消耗进行优化。在主流CNN基准测试中，我们实现了理论加速极限的99%以上，数据传输开销低于4%。