Design of Mixed-Signal LSI with Analog Spiking Neural Network and Digital Inference Circuits for Reservoir Computing

Edge computing requires low-power, real-time processing of complex information. Spiking neural networks are highly expected to be applied to edge computing due to their efficient computing properties. Here, we design a mixed-signal LSI consisting of an analog spiking neural network and digital inference circuits for edge device. The two-variable spiking neuron circuits operate in an analog manner using the physical properties of transistors and are connected through synaptic circuits to form a network. The neural network successfully operates and exhibits complex nonlinear behavior in response to external inputs. The power consumption of the spike generation is as low as tens of femtojoules per spike because the transistors in the circuits operate in the subthreshold region, which is enough to be used as edge computing devices. In addition, a digital circuit was designed to perform real-time inference using the spiking sequences from the analog spiking neural network. The result showed that the mixed-signal LSI consisting of the analog spiking neural network and the digital inference circuits can be applied to the spoken digit classification task in real time. The proposed system has the potential to be used as ultra-low-power neuromorphic hardware in practical applications.