Low-power Analog and Mixed-signal IC Design of Multiplexing Neural Encoder in Neuromorphic Computing

The research on computing clusters comprising neuromorphic systems has drawn the interest of many researchers in the field. Neural encoding is a crucial component that determines how the information is conveyed through a train of spikes, greatly impacting the mode of operations’ and systems’ performance to a large extent. Numerous encoding schemes have been proposed in the literature, including latency encoding, ISI encoding, and phase encoding. Each of these schemes has its own benefits and shortcomings which brings up the idea to see if they can complement each other. Multiplexing encoding combines two different schemes with the aim of enhancing the performance via conveying more information, making the encoded spikes more robust against noise. In this paper, we introduce a mixed-signal IC design of multiplexing latency-phase encoder. A key principle of the multiplexing encoding, the gamma alignment, is employed to achieve enhanced functionality of spiking neurons supported by biological research. In the proposed encoding scheme, a set of predetermined spiking neurons, which can be perceived as dimensionality reduction over the grouped higher-dimensional stimuli, maps the input currents to latency spike trains. Consequently, these spike trains are aligned and then superimposed on each other to form the resulting spike train. The simulation result is carefully inspected for verification of the encoder. The introduced power-efficient circuit is designed with 180nm CMOS technology and, to the best of our knowledge, is the first IC design of the multiplexing latency-phase that is built upon two different encoding schemes. The power consumption of the encoder is generally proportional to the number of neurons, and for a 4-neuron structure, the layout-level simulation result shows the circuit consumes 10mW of power.