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Integrated synapse devices for fluxon neural networks

Integrated synapse devices for fluxon neural networks
用于 Fluxon 神经网络的集成突触设备
批准号:
05650322
负责人:
NAKAJIMA Koji
金额:
$1.54万
依托单位:
依托单位国家:
日本
项目类别:
Grant-in-Aid for General Scientific Research (C)
财政年份:
1993
资助国家:
日本
项目状态:
已结题
起止时间:
1993 至 1994

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中文摘要
翻译
提出了一种基于squid的神经元超导电路和两种可变突触电路。约瑟夫森电路似乎更适合神经网络的VLSI,因为它可以在极低的功耗下高速运行。采用单结SQUID和双结SQUID的组合,实现了一个具有良好输入输出隔离和陡阈值特性的神经元电路。单结SQUID的量子态代表神经元状态,双结SQUID的输出电压为s型函数,工作在并联电阻的非锁存模式下。其中一个可变突触电路通过数字方式改变其电导值。另一种可变突触电路是可变电流源,其输出电流可以数字改变。两个突触回路都由多个分流的双结squid组成。除了电路特性的数值模拟外,我们还利用Nb/AIOx/Nb Josephson结技术制作了超导神经芯片。成功地演示了各元件和3位A/D转换器的基本工作原理。该A/D转换器的模拟输入频率高达100kHz,受我们的高增益测量设备的限制。仿真表明,该网络对100MHz以上的模拟输入有响应。以如此高的速度测试网络是我们未来的挑战之一。讨论了一种基于Hebb规则的变电流源型突触学习系统。
英文摘要
Novel superconducting circuits for a neuron and two types of variable synapses, which are based on SQUIDs, are presented. Josephson circuits seem to be superior for VLSI of the neural networks because of the high-speed operation under very low power dissipation. A neuron circuit with good input-output isolation and steep threshold characteristics is accomplished using a combination of a single-junction SQUID coupled to a double-junction SQUID.The quantum state of the single-junction SQUID represents the neuron state, and output voltage of the double-junction SQUID,which is operated under a nonlatching mode with shunt resistors, is a sigmoid-shaped function. One of the variable synapse circuits changes its conductance value digitally. Another variable synapse circuit is a variable current source in which the output current can change digitally. Both synapse circuits consist of multiple shunted double-junction SQUIDs. Besides numerical simulations of the circuit characteristics, we have fabricated superconducting neural chips using a Nb/AIOx/Nb Josephson junction technology. The fundamental operation of each element and 3-bit A/D converter are successfully demonstrated. This A/D converter was operated with analog input frequency as high as 100kHz, limited by our high-gain measurement equipment. Simulation shows that this network responds to analog input at over 100MHz. Testing the network at such high speeds is one of our challenges in the future. A learning system based on Hebb's rule with variable-current-source type of synapse is also discussed.
期刊论文(48)
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会议论文
DOI: --
发表时间:
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作者: []
通讯作者:
Y.Mizugaki: "Implementation of superconducting synapse into a neuro-based analog-to-digital converter" Appl.Phys.Lett.65. 1712-1713 (1994)
Y.Mizugaki:“将超导突触实现到基于神经的模数转换器”Appl.Phys.Lett.65。
DOI: --
发表时间:
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通讯作者:
S.Sato: "LSI Neural Chip of Pulse-Output Network with Programmable Synapse" IEICE Trans.ELECTRON.E78-C. 94-100 (1995)
S.Sato:“具有可编程突触的脉冲输出网络LSI神经芯片”IEICE Trans.ELECTRON.E78-C。
DOI: --
发表时间:
期刊:
影响因子: --
作者: []
通讯作者:
K. Nakajima: "Correct reaction neural network" Neural Networks. 6. 217-222 (1993)
K. Nakajima:“正确反应神经网络”神经网络。
DOI: --
发表时间:
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通讯作者:
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