ReHarvest: an ADC Resource-Harvesting Crossbar Architecture for ReRAM-Based DNN Accelerators

Abstract

ReRAM-based Processing-In-Memory (PIM) architectures have been increasingly explored to accelerate various Deep Neural Network (DNN) applications because they can achieve extremely high performance and energy-efficiency for in-situ analog Matrix-Vector Multiplication (MVM) operations. However, since ReRAM crossbar arrays’ peripheral circuits–analog-to-digital converters (ADCs) often feature high latency and low area efficiency, AD conversion has become a performance bottleneck of in-situ analog MVMs. Moreover, since each crossbar array is tightly coupled with very limited ADCs in current ReRAM-based PIM architectures, the scarce ADC resource is often underutilized.

In this paper, we propose ReHarvest, an ADC-crossbar decoupled architecture to improve the utilization of ADC resource. Particularly, we design a many-to-many mapping structure between crossbars and ADCs to share all ADCs in a tile as a resource pool, and thus one crossbar array can harvest much more ADCs to parallelize the AD conversion for each MVM operation. Moreover, we propose a multi-tile matrix mapping (MTMM) scheme to further improve the ADC utilization across multiple tiles by enhancing data parallelism. To support fine-grained data dispatching for the MTMM, we also design a bus-based interconnection network to multicast input vectors among multiple tiles, and thus eliminate data redundancy and potential network congestion during multicasting. Extensive experimental results show that ReHarvest can improve the ADC utilization by 3.2 ×, and achieve 3.5 × performance speedup while reducing the ReRAM resource consumption by 3.1 × on average compared with the state-of-the-art PIM architecture–FORMS.