Neural LSI's Possessing Autonomous Defect Self-repairing Capability

神经LSI具备自主缺陷自我修复能力

基本信息

批准号：
10450131
负责人：
YASUNAGA Moritoshi
金额：
$ 2.88万
依托单位：
University of Tsukuba
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B).
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

We have chosen the self-organizing map (SOM) as the target network in the neural networks, and evaluated its autonomous fault repairing capability quantitatively. Especially, in the project, we have proposed a defect model in which the defective neurons output arbitrary stuck values. From the analysis of the model, the following facts are shown (proved).1) The SOM can repair the defective neurons autonomously, if the defective neurons'outputs are larger than the critical stuck output, which is derived from the proposed defect model.2) The new criteria "critical stuck output" can be used widely even in the real applications such as image compression and face image recognition.Furthermore, in the project, we have also evaluated fault tolerance of the evolutionary algorithm (genetic algorithms, genetic program etc.) because the high fault tolerance can be also expected not only in the neural networks but also other algorithms based on the biological information processing. In order to evaluate the fault tolerance, we have carried out fault injection experiments using simulation programs and the prototype machine constructed based on reconfigurable LSI (FPGA). From the experimental results, it has been shown that the hardware based on the evolutionary algorithms also has high fault tolerance and graceful degradation against defective circuitsFrom wll those experimental results we have show that neural network LSIs and evolutionary algorithms-based LSIs has high fault tolerance and they can repair the defective circuits autonomously.

我们选择了自组织图（SOM）作为神经网络中的目标网络，并定量评估了其自主断层修复能力。特别是在项目中，我们提出了一个缺陷模型，其中有缺陷的神经元输出任意卡住的值。从对模型的分析中，显示以下事实（证明）。1）SOM可以自主修复有缺陷的神经元，如果有缺陷的神经元的输出大于关键卡住的输出，该输出是从提出的缺陷模型中得出的。由于不仅在神经网络中，而且还基于生物信息处理，不仅可以预期高容错性，而且还可以期望高可容忍度的耐受性（遗传算法，遗传程序等）的耐受性。为了评估容错，我们使用模拟程序和基于可重构LSI（FPGA）构建的原型机进行了故障注射实验。从实验结果中可以证明，基于进化算法的硬件也具有很高的容错性和优雅的降解，以防止有缺陷的循环，从这些实验结果我们已经表明，基于神经网络LSIS和基于进化算法的LSIS LSIS的LSIS和基于进化算法的LSIS具有高容错性，并且它们可以修复有缺陷的循环自动性。