Fully Hardware Memristive Neuromorphic Computing Enabled by the Integration of Trainable Dendritic Neurons and High‐Density RRAM Chip

Abstract Computing‐in‐memory (CIM) architecture inspired by the hierarchy of human brain is proposed to resolve the von Neumann bottleneck and boost acceleration of artificial intelligence. Whereas remarkable progress has been achieved for CIM, making further improvements in CIM performance is becoming increasingly challenging, which is mainly caused by the disparity between rapid evolution of synaptic arrays and relatively slow progress in building efficient neuronal devices. Specifically, dedicated efforts are required toward developments of more advanced activation units in terms of both optimized algorithms and innovative hardware implementations. Here a novel bio‐inspired dendrite function‐like neuron based on negative‐differential‐resistance (NDR) behavior is reported and experimentally demonstrates this design as a more efficient neuron. By integrating electrochemical random‐access memory (ECRAM) with ionic regulation, the tunable NDR neuron can be trained to enhance neural network performances. Furthermore, based on a high‐density RRAM chip, fully hardware implementation of CIM is experimentally demonstrated by integrating NDR neuron devices with only a 1.03% accuracy loss. This work provides 516 × and 1.3 × 10 5 × improvements on LAE (Latency‐Area‐Energy) property, compared to the digital and analog CMOS activation circuits, respectively. With device‐algorithm co‐optimization, this work proposes a compact and energy‐efficient solution that pushes CIM‐based neuromorphic computing into a new paradigm.