3D-FPIM: An Extreme Energy-Efficient DNN Acceleration System Using 3D NAND Flash-Based In-Situ PIM Unit
Hunjun Lee, Minseop Kim, Dongmoon Min, Joonsung Kim, Jongwon Back, Ho-Nam Yoo, Jong‐Ho Lee, Jangwoo Kim
Abstract
The crossbar structure of the nonvolatile memory enables highly parallel and energy-efficient analog matrix-vector-multiply (MVM) operations. To exploit its efficiency, existing works design a mixed-signal deep neural network (DNN) accelerator, which offloads low-precision MVM operations to the memory array. However, they fail to accurately and efficiently support the low-precision networks due to their naive ADC designs. In addition, they cannot be applied to the latest technology nodes due to their premature RRAM-based memory array.In this work, we present 3D-FPIM, an energy-efficient and robust mixed-signal DNN acceleration system. 3D-FPIM is a full-stack 3D NAND flash-based architecture to accurately deploy low-precision networks. We design the hardware stack by carefully architecting a specialized analog-to-digital conversion method and utilizing the three-dimensional structure to achieve high accuracy, energy efficiency, and robustness. To accurately and efficiently deploy the networks, we provide a DNN retraining framework and a customized compiler. For evaluation, we implement an industry-validated circuit-level simulator. The result shows that 3D-FPIM achieves an average of 2.09x higher performance per area and 13.18x higher energy efficiency compared to the baseline 2D RRAM-based accelerator.