CMOS Circuit Implementation of Spiking Neural Network for Pattern Recognition Using On-chip Unsupervised STDP Learning

Vohra, Sahibia Kaur; Thomas, Sherin Ann; Sakare, Mahendra; Das, Devarshi Mrinal

doi:10.48550/arxiv.2204.04430

Cited by 2 publications

(3 citation statements)

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“…For the chosen synaptic current, eNeuron3 has the fewest spikes in time compared to the other eNeurons [10], which results in the highest σ J over spike occurrences. Despite the four types of eNeurons described in the literature and used for STDP learning [6], [14], the obtained values for σ J are either of the same order of magnitude or exceed ∆T s .…”

Section: B Period Jittermentioning

confidence: 80%

“…In SNNs using analog eNeurons with high f spike , STDP learning curve can be illustrated in Fig. 2 [5], [6], [14]. There, a positive ∆T s less than 1 µs leads to a substantial increase in ∆w syn , while a negative ∆T s higher than -1 µs leads to a strong decrease in ∆w syn .…”

Section: B Stdp Learningmentioning

confidence: 99%

“…The proposed synapse supports the STDP rule in an energy and area efficient manner. Another SNN designed in a 180 nm CMOS technology is proposed in [14]. It uses the STDP learning to perform heart rate classification.…”

Section: B Stdp Learningmentioning

confidence: 99%

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Jitter Noise Impact on Analog Spiking Neural Networks: STDP Limitations

Jouni,

Rioufol,

Wang

et al. 2023

2023 36th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI)

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Accurate spike timing of neurons is essential for the temporal learning rules in Spiking Neural Networks (SNNs), such as the spike-timing dependent plasticity (STDP). Implementing these rules in analog SNNs presents a challenge as it would require a reliable spike timing of analog neurons. This paper investigates the impact of jitter noise on the spike timing of various analog neuron models, and highlights trade-offs in training neurons using spike timing. Post-layout simulation results reveal that noise-induced period jitter significantly affects spike occurrence time. Its values are three orders of magnitude higher than the precise spike timing required for an accurate synaptic weight updates. Moreover, the spike timing becomes a random variable with a σ /µ that exceeds 76%. This study shows that the spiking frequency is a more reliable measure of neuronal activity, as it achieves a σ /µ of only 0.2%.

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Section: B Period Jittermentioning

confidence: 80%

Section: B Stdp Learningmentioning

confidence: 99%