基于自旋电子器件随机性的神经网络认知系统

项目名称： 基于自旋电子器件随机性的神经网络认知系统

项目编号： No.61471015

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 张有光

作者单位： 北京航空航天大学

项目金额： 80万元

中文摘要： 当前集成电路面临高功耗、低可靠性等瓶颈，计算能力难以继续提高，新型信息器件及新型计算方法成为研究热点。由于自旋电子器件具有非易失存储、无限次高速读写、易于与导体工艺集成等优点，被广泛认为是解决功耗瓶颈的关键技术。人工神经网络可以进行超低功耗及高容错性计算，是未来智能芯片的主要计算构架之一。因此本课题基于新型自旋电子器件--自旋转移动量矩非易失磁性随机存储器（STT-MRAM）构建人工神经网路的突触，创造性地利用其在短编程脉冲下固有的、可控的随机特性，实现低功耗认知应用。我们将对磁性隧道结（MTJ）的随机特性进行研究和建模；结合机器学习与仿生学领域的已有成果设计和演示利用该随机特性的突触学习规则；模拟对现实生活中的应用，设计并制造一个小型演示器。本课题将以一种新的方法实现人工神经网络的低功耗认知应用，对人工智能研发和认知应用提供一种十分有益的探索。

中文关键词： 自旋电子学；非易失信息随机存储；磁隧道结；纳米突触；人工神经网络

英文摘要： Nowadays, the development of integrated circuit suffers from many problems such as high power consumption and low reliability, which has been the bottleneck in computing power. Therefore, new information devices and computing methods have stimulated a growing interest. Since spin-transfer torque magnetic RAM (STT-MRAM) based on magnetic tunnel junction (MTJ) devices feature non-volatility, high write and read speed and infinite endurance, STT-MRAM is a critical component to solve the consumption bottleneck. Artificial neural network can work in ultra-low power consumption and high fault-tolerant computing, which is one of the critical components of future smart-chips. This project aims at using magnetic RAM devices in an original probabilistic regime, to demonstrate low-power cognitive-type applications. This project will bring a radically novel point of view that may transform this emerging field. The programming of many nanodevices, and in particular magnetic RAM (MRAM), has an intrinsic random character. Low-energy programming pulses may program the device, but only with a finite probability. We propose to exploit this random aspect of short programming pulses to develop new ultra low power computing paradigms. Our research will focus on Spin Torque Transfer MRAMs. In these devices, the stochastic behavior is indeed controllable. We will aim bioinspired applications, where nanodevices are used similarly to biological synapses, and also other learning systems where synapses learn in ways inspired by machine learning. The final goal of this technology is to develop ultralow power embedded systems capable of extreme adaptation thanks to learning, and capable of processing natural data.The results of this project can have a strong social and economic impact. It is expected that a driver for future electronic devices will be ambient and cognitive intelligent devices that should simplify people's everyday life. They will require electronic systems, which can compute efficiently with the real-life data from sensors with minimum power consumption. Our novel computing paradigm can be a key tool to achieve such systems..

英文关键词： spintronics;NVRAM;STT-MTJ;nanosynapse;artificial neural network