Introduction of Non-Volatile Computing In Memory (nvCIM) by 3D NAND Flash for Inference Accelerator of Deep Neural Network (DNN) and the Read Disturb Reliability Evaluation : (Invited Paper)

In this paper, we introduce the optimal design methods of 3D NAND nvCIM [1], and then address the read disturb reliability issue. In recent years CIM [2] is widely considered as a promising solution to accelerate the DNN inference hardware. Theoretically, nvCIM can drastically reduce the power consumption by data movement because of no need to move the weights during computation. 3D NAND has the advantage of extremely low Icell (~nA), while the large ON/OFF ratio provides the capability to sum >10’000 cells together to improve the performance bandwidth and energy efficiency. We think that 3D NAND nvCIM has the potential to serve as the inference accelerator for the high-density fully- connected (FC) network which often requires high-bandwidth inputs. The read disturb property is studied. It is suggested that the "on-the-fly" calibration technique can well maintain the inference accuracy for 10-year usage.