Details

Autor(en) / Beteiligte

• Joo, Bomin
• Han, Jin-Woo
• Kong, Bai-Sun

Titel

Energy- and Area-Efficient CMOS Synapse and Neuron for Spiking Neural Networks With STDP Learning

Ist Teil von

• IEEE transactions on circuits and systems. I, Regular papers, 2022-09, Vol.69 (9), p.3632-3642

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• This paper proposes CMOS synapse and neuron for use in spiking neural networks to perform cognitive functions in a bio-inspired manner. The proposed synapse can trace the eligibility of the timing relationship between pre- and post-synaptic spikes, supporting a bio-plausible local learning rule called the spike timing-dependent plasticity (STDP) in an energy- and area-efficient manner. The proposed neuron can support neural functions such as synaptic current integration, threshold-based firing, neuronal leaking, membrane potential resetting, and adjustable refractory period with improved energy and area efficiency. The STDP curve shape of the synapse and the firing rate of the neuron can be adjusted as desired. Their variability due to process, voltage, and temperature (PVT) variations can also be minimized. The proposed CMOS neuron and synapse circuits were designed in a 28-nm CMOS process. The performance evaluation results indicate that the proposed synapse reduces energy consumption and area by up to 94% and 43% compared to conventional CMOS synapses. They also indicate that the proposed neuron achieves energy and area reductions of 37% and 23%, respectively, compared to conventional CMOS neurons. An associative neural network composed of the proposed neuron and synapse was designed to verify that they together work well for performing a cognitive function of associative learning and inferencing.