An Area-Efficient Integrate-and-Fire Neuron Circuit with Enhanced Robustness against Synapse Variability in Hardware Neural Network - Arati Kumari Shah; Kannan Udaya Mohanan; Jisun Park; Hyungsoon Shin; Eou-Sik Cho; Seongjae Cho (IET Circuits, Devices and Systems)
Neuron circuits are the fundamental building blocks in the modern neuromorphic system. Designing compact and low-power neuron circuits can significantly improve the overall area and energy efficiencies of a neuromorphic chip architecture. Here, practical neuron circuits must overcome the variations arising from nonideal behaviors of synaptic devices, such as stuck-at-fault and conductance deviation. In this study, a compact leaky integrate-and-fire neuron circuit has been designed, with resilience to synaptic device state variations, for hardware implementation of spiking neural networks (SNNs). The proposed neuron circuit is simulated on the 0.35-μm Si complementary metal-oxide-semiconductor technology node by a series of circuit simulations based on HSPICE. The proposed circuit occupies a reduced area and exhibits low power consumption (14.7 µW per spike). Furthermore, the optimized circuit design results in a high degree of tolerance toward input-current variations arising from conductance-state variations in the synapse array. Hence, the proposed neuron circuit would be capable of substantially improving the area efficiency and reliability in the realization of the hardware-oriented SNN architectures.