Execution Sequence Optimization for Processing In-Memory using Parallel Data Preparation

Processing in-memory (PIM) promises to unleash unprecedented computing capabilities for high-data-rate applications. Computation using PIM is performed by breaking down computationally expensive operations into in-memory kernels that can be efficiently executed using non-volatile memory. Logic styles such as MAGIC require that each output memory cell is prepared for evaluation before executing the functional logic operation. State-of-the-art synthesis algorithms perform the preparation immediately after memory cells have expired. Unfortunately, this results in that columns of cells are prepared greedily, instead of leveraging efficient parallel data preparation instructions. In this paper, we propose the PREP framework that maximizes the opportunities for parallel column preparation using execution sequence optimization. The key idea of the framework is to postpone data preparation instructions until there are no available prepared cells. Next, the accumulated memory cells are prepared in parallel to release the memory for functional evaluations. The framework is capable of exploring a frontier of area-performance solutions. The PREP framework is evaluated using 15 benchmarks from the SuiteSparse library. Compared with state-of-the-art synthesis tools, energy consumption and latency are respectively reduced by 27% and 25% with no additional cost in crossbar memory.