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A CMOS Axon-sharing Neuron Array with Background Calibration
Conference paper

A CMOS Axon-sharing Neuron Array with Background Calibration

Xiangao Qi, Jian Zhao, Guoxing Wang, Kea-Tiong Tang and Yongfu Li
Proceedings - IEEE International Symposium on Circuits and Systems, Vol.2022-May, pp.3443-3447
2022

Abstract

leaky integrate and fire (LIF) model neural computing hardware unit (NCHU) neuromorphic spiking neural network (SNN) Electrical and Electronic Engineering
The implementation of a power-efficient neuron array system with high throughput and controllable mismatch plays an important role in power-sensitive applications and brain simulations. This paper presents an array of 48 Integrate-and-Fire neurons with axon-sharing architecture implemented in 55-nm CMOS technology. The combination of log-domain circuits and comparator-sharing in neuron design achieves the integration of 3125 neurons/mm2 and power consumption of 5.3 pJ/spike. The proposed time modulated axon-sharing synapse architecture realizes 5500 events/s/neuron unit throughput. A novel background calibration module is integrated to reduce the mismatch between neurons. Simulations presents a 45% improvement in SD of inter-spike interval variation. Finally, we validate the architecture by implementing a spiking neural network for solving a 3-stage Sudoku Puzzle. 100% success rate is obtained after calibration.

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