Hardware Efficient Reconfigurable Logic-in-Memory Circuit Based Neural Network Computing

With the explosive growth in processing and data storage capability, computing in memory (CIM) technology is considered a feasible method to mitigate the memory wall. Re-cently, a floating-gate field-effect transistor (FGFET) technology with single-layer MoS2 channel was proposed, which can flexibly transfer the memory and logic gate by setting the corresponding voltage. In this paper, based on the new FGFETs devices, we propose a hardware-efficient reconfigurable logic-in-memory (LIM) circuit to perform neural network (NN) computing. We first represent the basic FGFET array as a matrix form. Thereby, the adders, multipliers, registers, and PE units are designed by the matrix function of the arrays. The proposed circuits can dynamically transfer the memory to computing logic online when fewer data are required to be stored. A theoretical experiment based on the VGG16 network exhibits an efficiency increase of 85.35% in the same logic area compared with the traditional method.