Deep spiking neural networks with integrate and fire neuron using steep switching device

IF 1.4 4区 物理与天体物理 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Solid-state Electronics Pub Date : 2024-01-12 DOI:10.1016/j.sse.2024.108860
Sung Yun Woo , Sangyeon Pak , Sung-Tae Lee
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Abstract

Deep learning has shown impressive capabilities in tasks like speech recognition and image classification. However, modern deep neural networks often demand a significant number of weights and extensive computational resources, creating efficiency challenges for applications on edge devices. To address these issues, researchers have introduced deep spiking neural networks (DSNNs) that leverage specialized hardware for synapses and neurons. DSNNs offer a potential solution by improving efficiency in edge-device applications. In this paper, the hardware based DSNN with integrate and fire neuron using steep switching device was investigated. We propose integrate and fire neuron using steep switching device to implement rate coding as input encoding method. Because the steep switching device has double-gate, the threshold voltage of the neuron circuits can be adaptively controlled, which changes the rates of input pulse. Hence, the adjustment of the threshold of neuron can be employed to mitigate the accuracy deterioration resulting from the transformation from deep neural networks (DNNs) to DSNNs. In addition, the off-current of proposed integrate and fire neuron circuit decreases significantly as the steep switching device has steep subthreshold swing. A system simulation of a hardware based DSNN shows that the adjustable threshold of the neuron circuit can achieve a high inference accuracy of 98.36 % which is comparable to that obtained with software based DNN.

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利用陡峭开关设备整合和发射神经元的深度尖峰神经网络
深度学习已在语音识别和图像分类等任务中展现出令人印象深刻的能力。然而,现代深度神经网络往往需要大量权重和大量计算资源,给边缘设备上的应用带来了效率挑战。为了解决这些问题,研究人员推出了深度尖峰神经网络(DSNN),利用专门的硬件来实现突触和神经元。DSNN 通过提高边缘设备应用的效率,提供了一种潜在的解决方案。本文研究了基于硬件的 DSNN,该网络使用陡峭开关设备集成和发射神经元。我们提出了使用陡峭开关器件的集成和发射神经元,以实现率编码作为输入编码方法。由于陡峭开关器件具有双栅极,神经元电路的阈值电压可以自适应控制,从而改变输入脉冲的速率。因此,神经元阈值的调整可用于缓解从深度神经网络(DNN)到 DSNN 的转变所导致的精度下降。此外,由于陡峭开关器件具有陡峭的阈下摆动,因此所提议的集成和发射神经元电路的关断电流会显著降低。对基于硬件的 DSNN 进行的系统仿真表明,神经元电路的可调阈值可实现 98.36 % 的高推理精度,与基于软件的 DNN 的推理精度相当。
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来源期刊
Solid-state Electronics
Solid-state Electronics 物理-工程:电子与电气
CiteScore
3.00
自引率
5.90%
发文量
212
审稿时长
3 months
期刊介绍: It is the aim of this journal to bring together in one publication outstanding papers reporting new and original work in the following areas: (1) applications of solid-state physics and technology to electronics and optoelectronics, including theory and device design; (2) optical, electrical, morphological characterization techniques and parameter extraction of devices; (3) fabrication of semiconductor devices, and also device-related materials growth, measurement and evaluation; (4) the physics and modeling of submicron and nanoscale microelectronic and optoelectronic devices, including processing, measurement, and performance evaluation; (5) applications of numerical methods to the modeling and simulation of solid-state devices and processes; and (6) nanoscale electronic and optoelectronic devices, photovoltaics, sensors, and MEMS based on semiconductor and alternative electronic materials; (7) synthesis and electrooptical properties of materials for novel devices.
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