Efficient and Low Overhead Memristive Activation Circuit for Deep Learning Neural Networks

Anu Bala; Xiaohan Yang; Adedotun Adeyemo; Abusaleh Jabir

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Efficient and Low Overhead Memristive Activation Circuit for Deep Learning Neural Networks

机译：深度学习神经网络的高效和低开销椎间膜激活电路

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摘要

An efficient memristor MIN function based activation circuit is presented for memristive neuromorphic systems, using only two memristors and a comparator. The ReLU activation function is approximated using this circuit. The ReLU activation function helps to significantly reduce the time and computational cost of training in neuromorphic systems due to its simplicity and effectiveness in deep neural networks. A multilayer neural network is simulated using this activation circuit in addition to traditional memristor crossbar arrays. The results illustrate that the proposed circuit is able to perform training effectively with significant savings in time and area in memristor crossbar based neural networks.

机译：仅使用两个忆阻器和比较器的椎间膜神经晶体系统给出了一种有效的映射器MIN功能基于激活电路。使用该电路近似Relu激活函数。由于深神经网络中的简单性和有效性，Relu激活功能有助于显着降低神经胸系统中训练的时间和计算成本。除了传统的Memristor Crossbar阵列之外，使用该激活电路模拟多层神经网络。结果说明了所提出的电路能够在基于Memristor横杆的神经网络中有效地节省时间和区域来执行训练。

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来源
《Journal of Low Power Electronics》 |2019年第2期|共10页
作者
Anu Bala; Xiaohan Yang; Adedotun Adeyemo; Abusaleh Jabir;
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作者单位

School of Engineering Computing and Mathematics;

School of Engineering Computing and Mathematics;

School of Engineering Computing and Mathematics;

School of Engineering Computing and Mathematics;

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原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
Neural network; MIN-MAX function; Memristor crossbar array; ReLU;

机译：神经网络;最大功能;Memristor CrossBar阵列;relu.;

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1. Efficient and Low Overhead Memristive Activation Circuit for Deep Learning Neural Networks [J] . Anu Bala, Xiaohan Yang, Adedotun Adeyemo, Journal of Low Power Electronics . 2019,第2期

机译：深度学习神经网络的高效和低开销椎间膜激活电路
2. Memristive Quantized Neural Networks: A Novel Approach to Accelerate Deep Learning On-Chip [J] . Zhang Yang, Cui Menglin, Shen Linlin, Cybernetics, IEEE Transactions on . 2021,第4期

机译：回忆量化神经网络：一种加快芯片深度学习的新方法
3. An efficient facial emotion recognition system using novel deep learning neural network-regression activation classifier [J] . Devi Anjani Suputri D., Satyanarayana Ch Multimedia Tools and Applications . 2021,第12期

机译：利用新型深度学习神经网络回归激活分类器的高效面部情感识别系统
4. A Memristive Activation Circuit for Deep Learning Neural Networks [C] . Anu Bala, Xiaohan Yang, Adedotun Adeyemo, . 2018

机译：深度学习神经网络的忆阻激活电路
5. Emerging Opportunities in Machine Learning Hardware Acceleration: From Advanced Neural Networks Implementation to Ultra-efficient Deep Learning Framework Using Next Generation Technology [D] . ?Cai, Ruizhe 2020

机译：机器学习硬件加速的新兴机会：从先进的神经网络实现，使用下一代技术实现超高效的深度学习框架
6. Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks [O] . Carlos Garcia-Perez, Keiichi Ito, Javier Geijo, 2021

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7. Efficient and Low Overhead Memristive Activation Circuit for Deep Learning Neural Networks [O] . Anu Bala, Xiaohan Yang, Adedotun Adeyemo, 2019

机译：深度学习神经网络的高效且低架空椎相激活电路

Efficient and Low Overhead Memristive Activation Circuit for Deep Learning Neural Networks

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