Processing-in-Memory in ReRAM-based Main Memory

Process in memory (PIM) is a promising solution to address the “memory wall” issue, and most of the proposed PIM architectures integrate logic with memory. However, as long as the computation is done by logic, it needs to access memory for data. In this work, we propose a novel PIM system, utilizing metal-oxide resistive random access memory (ReRAM) as main memory and also for computation. As an emerging non-volatile memory, ReRAM has been considered as a promising candidate for future memory architecture, thanks to its high density, fast read access, and low leakage power. In addition, it has the capability to represent synaptic weights and execute neural networks with its crossbar architecture. Our proposed design is based on a ReRAM-based main memory system. In the proposed design, a portion of ReRAM crossbar arrays can be configured either as normal memory or to be used for neural computation (hence a morphable PIM structure). We design the required peripheral circuits by computing carefully to minimize the area overhead, and also to provide a hardware-software interface for the developers so that they can easily configure the proposed design for various neural network tasks. We evaluate the proposed design on two sets of benchmarks, and compare it with a CPU-only solution and two neural process unit (NPU) solutions (using NPUs as co-processors and as PIMprocessors through 3D stacking). The experiment results show that the proposed design achieve the best energyefficiency with the highest speedup, which is ∼9000× faster than the CPU-only solution for large neural networks. In addition, it is also a low-overhead PIM solution, with only < 3.8% area overhead compared to the normal ReRAM chips.