Hybrid functional 3D artificial synapses for convolution and reinforcement learning

Recent advancements in compute-in-memory (CIM) technology have garnered substantial interest, representing a promising shift in the computing paradigm. While conventional CIM systems using two-dimensional (2D) crossbar arrays have shown remarkable area and energy efficiency in fully connected neural networks, their applicability to convolutional neural networks remains inefficient. Herein, we introduce an architecture that enables convolution and reinforcement learning by leveraging the unique configuration of vertically stacked 3D flash memory. The proposed 3D convolution block (3D CB) significantly reduces area overhead and enhances the reliability and energy efficiency of convolution operations. The 3D CB and 3D fully connected block (3D FCB) exhibit distinct functionalities with minimal structural modifications. The compatibility between the 3D CB and the 3D FCB facilitates their seamless co-integration on a single wafer. The fully integrated system presented in this study demonstrates precise and consistent path planning capabilities for autonomous driving with high area and energy efficiency, paving the way for advanced CIM technology.