Reliability-Aware Design of Spike-Event Neuromorphic Circuits

Shaik, Jani Babu; Picardo, Siona Menezes; Singhal, Sonal; Goel, Nilesh

doi:10.36227/techrxiv.15141537.v1

Cited by 2 publications

(3 citation statements)

References 16 publications

(6 reference statements)

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“…A reduction in V TH shift can be achieved by employing a rel-SLIF neuron in place of the conv-SLIF neuron. 40 In the rel-SLIF neuron circuit, a diode-connected transistor PM4 is added to mitigate degradation in transistor PM1. Figure 11 shows rel-SLIF neuron.…”

Section: Reliability-aware Temporal Neuromorphic Encodermentioning

confidence: 99%

“…It is reported that the rel-SLIF neuron consumes less power than the conv-SLIF neuron. 40 This is because the addition of the transistor PM4 lowers the voltage swing of the inverter output, which reduces the power consumption.…”

Section: Reliability-aware Temporal Neuromorphic Encodermentioning

confidence: 99%

“…It is observed from figure that power dissipated by rel-encoder is always lower than conv-encoder for entire V IN range. It is reported that the rel-SLIF neuron consumes less power than the conv-SLIF neuron 40. This is because the addition of the transistor PM4 lowers the voltage swing of the inverter output, which reduces the power consumption.…”

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confidence: 99%

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Reliability‐aware design of temporal neuromorphic encoder for image recognition

Shaik

Singhal

et al. 2021

Circuit Theory & Apps

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Very large scale integration (VLSI)-based neuromorphic systems have been evolving quickly in recent years. These systems have been used in complex cognitive tasks such as image classification and pattern recognition. A neuromorphic encoder is an essential component in a neuromorphic system, which converts sensory data into spike trains. The advancement in CMOS technology nodes leads to various reliability issues. Bias temperature instability (BTI) and hot carrier injection (HCI) are major reliability issues in analog/ mixed VLSI circuits. This paper implements a temporal neuromorphic encoder, and a corresponding mathematical model is derived for image processing applications. The relationship between an input image pixel value and output of the encoder, that is, interspike interval (ISI), is found to be exponential. The impact of BTI and HCI on the temporal neuromorphic encoder is analyzed. The degradation analysis revealed a loss of encoding functionality for which three mitigation techniques are discussed. Finally, a reliability-aware neuromorphic encoder is proposed to minimize the effect of degradation over its lifetime. The power consumption of conventional and proposed reliabilityaware neuromorphic encoders is also presented.

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Section: Reliability-aware Temporal Neuromorphic Encodermentioning

confidence: 99%

Section: Reliability-aware Temporal Neuromorphic Encodermentioning

confidence: 99%