Reliability-Aware Design of Spike-Event Neuromorphic Circuits

Very Large Scale Integration (VLSI) based neuromorphic circuits also known as Silicon Neurons (SiNs) emulate the electrophysiological behavior of biological neurons. With the advancement in technology, neuromorphic systems also lead to various reliability issues and hence making their study important. Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI) are the two major reliability issues present in VLSI circuits. In this work, we have investigated the combined effect of BTI and HCI on the two types of integrate-and-fire based SiNs namely (a) Axon-Hillock and (b) Simplified Leaky integrate-and-fire circuits using their key performance parameters. Novel reliability-aware AH and SLIF circuits are proposed to mitigate the reliability issues. Proposed reliability-aware designs show negligible deviation in performance parameters after aging. The time-zero process variability analysis is also carried out for proposed reliability-aware SiNs. The power consumption of existing and proposed reliability-aware neuron circuits is analyzed and compared.<br>

Reliability-Aware Design of Spike-Event Neuromorphic Circuits

Very Large Scale Integration (VLSI) based neuromorphic circuits also known as Silicon Neurons (SiNs) emulate the electrophysiological behavior of biological neurons. With the advancement in technology, neuromorphic systems also lead to various reliability issues and hence making their study important. Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI) are the two major reliability issues present in VLSI circuits. In this work, we have investigated the combined effect of BTI and HCI on the two types of integrate-and-fire based SiNs namely (a) Axon-Hillock and (b) Simplified Leaky integrate-and-fire circuits using their key performance parameters. Novel reliability-aware AH and SLIF circuits are proposed to mitigate the reliability issues. Proposed reliability-aware designs show negligible deviation in performance parameters after aging. The time-zero process variability analysis is also carried out for proposed reliability-aware SiNs. The power consumption of existing and proposed reliability-aware neuron circuits is analyzed and compared.<br>

Emerging Artificial Neuron Devices for Probabilistic Computing

In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.