Solving Overlapping Pattern Issues in On-Chip Learning of Bio-Inspired Neuromorphic System with Synaptic Transistors

Kim, Hyungjin; Park, Byung‐Gook

doi:10.3390/electronics9010013

Cited by 3 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NOR-type synaptic device is modeled with a Voltage-Controlled Current Source (VCCS) behavioral model with 3 terminals (gate, drain, source) as shown in Fig. 5 (a) [52,53]. The current between the drain and the source is determined by the voltage difference between the gate and the source.…”

Section: A Snn Architecturementioning

confidence: 99%

Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding

Kwon

Yeom

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Hardware-based Spiking Neural Networks (SNNs) are regarded as promising candidates for the cognitive computing system due to its low power consumption and highly parallel operation. In this paper, we train the SNN in which the firing time carries information using temporal backpropagation. The temporally encoded SNN with 512 hidden neurons achieved an accuracy of 96.90% for the MNIST test set. Furthermore, the effect of the device variation on the accuracy in temporally encoded SNN is investigated and compared with that of the rate-encoded network. In a hardware configuration of our SNN, NOR-type analog memory having an asymmetric floating gate is used as a synaptic device. In addition, we propose a neuron circuit including a refractory period generator for temporally encoded SNN. The performance of the 2-layer neural network composed of synapses and proposed neurons is evaluated through circuit simulation using SPICE based on the BSIM3v3 model with 0.35 μm technology. The network with 128 hidden neurons achieved an accuracy of 94.9%, a 0.1% reduction compared to that of the system simulation of the MNIST dataset. Finally, each block's latency and power consumption constituting the temporal network is analyzed and compared with those of the rate-encoded network depending on the total time step. Assuming that the network has 256 total time steps, the temporal network consumes 15.12 times less power than the rateencoded network and makes decisions 5.68 times faster.

show abstract

Section: A Snn Architecturementioning

confidence: 99%