SSR: A Skeleton-based Synthesis Flow for Hybrid Processing-in-RRAM Modes

Recently, the emerging resistive random access memory (RRAM) shows its potential to construct a processing-in-memory (PIM) architecture. It supports a variety of computation modes, including the digital mode and the analog mode. Both modes can perform parallel computation inside an RRAM crossbar. However, the lack of automatic synthesis flow limits their application scenarios. Although previous works implement several large-scale applications, e.g., image processing algorithms and neural networks, using these two modes, most of their implementations are designed manually or semi-manually. In our view, the lack of a specific application representation is a limiting factor for developing a synthesis flow. Therefore, in this work, we propose the skeleton as an application representation. Users can model applications and their potential parallelism in RRAM with nested skeletons and primitive operations. Then, we propose SSR, a skeleton-based flow that can automatically synthesize large-scale applications to RRAM crossbars. For an application represented in skeletons, SSR first partitions it into the digital part and the potential analog part. After that, SSR optimizes primitive operations and allocates bounding boxes to skeletons for both parts under the guide of pre-synthesis results. Finally, SSR maps bounding boxes of skeletons onto crossbars to enable pipelined computation. Experimental evaluations on several popular applications show that SSR improves throughput, latency, and area multiple times over previous works.