scholarly journals Area- and Energy-Efficient STDP Learning Algorithm for Spiking Neural Network SoC

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 216922-216932
Author(s):  
Giseok Kim ◽  
Kiryong Kim ◽  
Sara Choi ◽  
Hyo Jung Jang ◽  
Seong-Ook Jung
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Learning Algorithm ◽  
Spiking Neural Network

scholarly journals Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3276
Author(s):  
Szymon Szczęsny ◽  
Damian Huderek ◽  
Łukasz Przyborowski
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Waveform Analysis ◽  
Spiking Neural Network ◽  
Synaptic Connections ◽  
Practical Application ◽  
Accuracy Parameter ◽  
The Stability ◽  
Time Waveform ◽  
Network Mapping

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

Deep Spiking Neural Network for High-Accuracy and Energy-Efficient Face Action Unit Recognition

2021 ◽  
Author(s):  
Zhang Jingren ◽  
Wang Jingjing ◽  
Yan Jingwei ◽  
Wang Chunmao ◽  
Pu Shiliang
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
High Accuracy ◽  
Spiking Neural Network ◽  
Action Unit

scholarly journals Simplified spiking neural network architecture and STDP learning algorithm applied to image classification

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
Taras Iakymchuk ◽  
Alfredo Rosado-Muñoz ◽  
Juan F Guerrero-Martínez ◽  
Manuel Bataller-Mompeán ◽  
Jose V Francés-Víllora
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Spiking Neural Network ◽  
Neural Network Architecture

scholarly journals Quantized Weight Transfer Method Using Spike-Timing-Dependent Plasticity for Hardware Spiking Neural Network

2021 ◽  
Vol 11 (5) ◽  
pp. 2059
Author(s):  
Sungmin Hwang ◽  
Hyungjin Kim ◽  
Byung-Gook Park
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Transfer Method ◽  
Artificial Neural Network Ann ◽  
Online Learning Algorithm ◽  
Weight Transfer

A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artificial neural network (ANN) are transferred to synaptic devices. However, it is time-consuming to map all the synaptic weights as the scale of the neural network increases. In this paper, we propose a method for quantized weight transfer using spike-timing-dependent plasticity (STDP) for hardware-based SNN. STDP is an online learning algorithm for SNN, but we utilize it as the weight transfer method. Firstly, we train SNN using the Modified National Institute of Standards and Technology (MNIST) dataset and perform weight quantization. Next, the quantized weights are mapped to the synaptic devices using STDP, by which all the synaptic weights connected to a neuron are transferred simultaneously, reducing the number of pulse steps. The performance of the proposed method is confirmed, and it is demonstrated that there is little reduction in the accuracy at more than a certain level of quantization, but the number of pulse steps for weight transfer substantially decreased. In addition, the effect of the device variation is verified.

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