A general purpose neurocomputer

F. B. Verona, P. De Pinto, F. Lauria, M. Sette
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引用次数: 3

Abstract

Presents a neural network, composed of linear units with threshold, as the CPU of a stored program MIMD architecture. The Caianiello formalism, is introduced as an aid to implement the arithmetic and control algorithms, needed for the smooth running of this general-purpose system. That is, in the neural net both the arithmetic and logic algorithms and the operating system have been implemented. The latter is diffuse as it has been co-implemented with the single arithmetic operations. It controls each operation I/O, the input, output and intermediate data buffers, the clerical work associated to the beginning and the end of a task execution, etc. The neural net control is data-driven, i.e. the incoming data are the very signals telling the net to execute its task. As the net is data-driven, the system supports an efficient run time resource allocation algorithm. That is, at run time the incoming instructions chase the available resources and the waiting time, spent by the data in presence of idle resources, is minimized. At the same time, the system pipelines, automatically, nested loops, of arbitrary depth, and accepts unlimited recursive calls of routines.<>
通用神经计算机
提出了一种由带阈值的线性单元组成的神经网络作为存储程序MIMD体系结构的CPU。引入Caianiello形式,作为实现该通用系统顺利运行所需的算术和控制算法的辅助工具。也就是说,在神经网络中,算法和逻辑算法以及操作系统都已经实现。后者是分散的,因为它是与单个算术运算共同实现的。它控制每个操作I/O,输入,输出和中间数据缓冲区,与任务执行的开始和结束相关的文书工作,等等。神经网络控制是数据驱动的,即输入的数据是告诉神经网络执行其任务的信号。由于网络是数据驱动的,系统支持一种高效的运行时资源分配算法。也就是说,在运行时,传入的指令追逐可用的资源,并且在存在空闲资源的情况下,数据所花费的等待时间被最小化。与此同时,系统自动管道,嵌套循环,任意深度,并接受无限递归调用例程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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