NandPIM: An Optimization Framework for 2D/3D NAND-Based PIM Featuring Genetic Algorithm-based Search of Design Space

This paper presents NandPIM, an innovative NAND flash-based processing-in-memory (PIM) accelerator design coupled with an automatic deep neural network (DNN) quantization framework. By integrating spatial partition mapping and weight duplication strategies, NandPIM fully leverages the high-density storage capability of NAND flash memory and significantly improves computational parallelism. Moreover, alternating mapping of weights within the flash memory array improves the effective endurance of the PIM system. The quantization framework of NandPIM, powered by a Genetic Algorithm, automatically optimizes the bit-widths of DNN weights to determine the optimal layer-by-layer quantization strategy within the constraints of hardware resources and inference accuracy. Experimental results demonstrate that the proposed design outperforms traditional PIM approaches in terms of area, latency and energy efficiency. Furthermore, NandPIM achieves automatic bit-width compression across various DNN models, boosting the computational efficiency while preserving the model accuracy. This framework provides a compelling solution for the efficient deployment of DNNs on 2D/3D NAND-based PIM chips.