脉冲神经流二进制算法

J. Aimone, A. Hill, William M. Severa, C. Vineyard
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引用次数: 4

摘要

布尔函数和二进制算术运算是标准计算范式的核心。因此,计算的许多进步都集中在如何使这些操作更有效以及探索它们可以计算什么上。为了最好地利用新的计算范式的优势,考虑它们提供哪些独特的计算方法是很重要的。然而,对于任何特殊用途的协处理器,布尔函数和二进制算术运算都非常有用,除了其他用途之外,还可以通过在设备上对数据进行预处理和后处理来避免不必要的协处理器I/O开关。这对于尖峰的神经形态结构来说尤其如此,因为这些基本的操作并不是基本的低级操作。相反,这些函数需要特定的实现。在这里,我们讨论了一种有利的流二进制编码方法的含义,以及一些设计用于精确计算基本布尔和二进制运算的电路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spiking Neural Streaming Binary Arithmetic
Boolean functions and binary arithmetic operations are central to standard computing paradigms. Accordingly, many advances in computing have focused upon how to make these operations more efficient as well as exploring what they can compute. To best leverage the advantages of novel computing paradigms it is important to consider what unique computing approaches they offer. However, for any special-purpose co-processor, Boolean functions and binary arithmetic operations are useful for, among other things, avoiding unnecessary I/O on-and-off the co-processor by pre- and post-processing data on-device. This is especially true for spiking neuromorphic architectures where these basic operations are not fundamental low-level operations. Instead, these functions require specific implementation. Here we discuss the implications of an advantageous streaming binary encoding method as well as a handful of circuits designed to exactly compute elementary Boolean and binary operations.
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