An Energy-Efficient Mixed-Bit CNN Accelerator with Column Parallel Readout for ReRAM-based In-memory Computing

Computing-In-memory (CIM) accelerators have the characteristics of storage and computing integration, which has the potential to break through the limit of Moore's law and the bottleneck of Von-Neumann architecture for convolutional neural networks (CNN) implementation improvement. However, the performance of CIM accelerators is still limited by conventional CNN architectures and inefficient readouts. To increase energy-efficient performance, an optimized CNN model is required and a low-power column parallel readout is necessary for edge-computing hardware. In this work, an ReRAM-based CNN accelerator is designed. Mixed-bit operations from 1 bit to 8 bits are supported by an effective bitwidth configuration scheme to implement Neural Architecture Search (NAS)-optimized layer-wise multi-bit CNNs. Besides, column-parallel readout is achieved with excellent energy-efficient performance by a variation-reduction accumulation mechanism and low-power readout circuits. Additionally, we further explore systolic data reuse in an ReRAM-based PE array. Experiments are implemented on NAS-optimized ResNet-18. Benchmarks show that the proposed ReRAM accelerator can achieve peak energy efficiency of 2490.32 TOPS/W for 1-bit operation and average energy efficiency of 479.37 TOPS/W for 1 similar to 8-bit operations with evaluating NAS-optimized multi-bitwidth CNNs. When compared with the state-of-the-art works, the proposed accelerator shows at least 14.18x improvement on energy efficiency.