论文标题
尖峰神经流二进制算术
Spiking Neural Streaming Binary Arithmetic
论文作者
论文摘要
布尔函数和二进制算术操作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的内容上。为了最好地利用新型计算范式的优势，重要的是要考虑它们提供的独特计算方法。但是，对于任何特殊用途的处理器，布尔函数和二进制算术操作对于避免通过预先处理前和后处理的数据避免了不必要的I/O在处理过程中的不必要的I/O。对于这些基本操作不是基本的低级操作的刺激神经形态架构尤其如此。相反，这些功能需要特定的实现。在这里，我们讨论了有利的流式二进制编码方法的含义以及少数电路，旨在精确计算基本布尔和二进制操作。
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.
