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引用次数: 1

摘要

本文讨论了数字VLSI系统中人工神经网络计算的特性。我们还评论了人工计算模型、学习算法和人工神经网络的数字实现。该分析适用于以各种位精度执行二进制整数运算的常规数组或处理元素。计算速率受到功耗的限制,这取决于所需的精度和封装约束,如引脚。它们还强烈依赖于CMOS技术的最小特征尺寸。我们强调具有低位精度的定制数字实现,因为这些电路需要更低的功率和硅面积。实现这一目标的一种方法是使用随机算法,基于元胞自动机电路生成伪随机数。
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Limits to neural computations in digital arrays
In this paper the properties of artificial neural network computations by digital VLSI systems are discussed. We also comment on artificial computational models, learning algorithms, and digital implementations of ANNs in general. The analysis applies to regular arrays or processing elements performing binary integer arithmetic at various bit precisions. Computation rates are limited by power dissipation which is dependent upon required precision and packaging constraints such as pinout. They also depend strongly on the minimum feature size of the CMOS technology. We emphasize custom digital implementations with low bit precision, because these circuits require reduced power and silicon area. One way this may be achieved is using stochastic arithmetic, with pseudorandom number generation based on cellular automata circuits.
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