具有可编程注意力机制的三维记忆深度神经网络

Hongyu An, Kangjun Bai, Yang Yi
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引用次数: 1

摘要

注意力机制是基于神经网络的视觉系统的一项关键功能,它能使人脑将计算精力用于处理所需的信息,同时有意忽略不想要的背景噪音。通过这种信号处理方法,大脑系统实现了显著的能效。研究注意力机制的意义不仅在于揭示人脑是如何理解视觉信号的,还在于构建一个以注意力为导向的高能效神经形态系统。本文提出了视觉注意力机制的假说,推导出神经网络模型的数学方程,并利用忆阻器和三维(3D)单片集成技术实现了电路的实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Three-dimensional Memristive Deep Neural Network with Programmable Attention Mechanism
Attention Mechanism is a critical function of a neural network-based visual system that enables the human brain can devote computing effort to process desired information, concurrently ignoring undesired background noise intentionally. Through this signal processing methodology, the brain system achieves a remarkable energy efficiency. The significance of investigating the attention mechanism is to not only potentially reveal how the human brain comprehends visual signals but also to construct an attention-oriented power-efficient neuromorphic system. This paper proposes a hypothesis for the visual attention mechanism, derives a neural network model with mathematical equations, and realizes the circuit implementation with memristor and three-dimensional (3D) monolithic integration technology.
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