Spiking Neural Streaming Binary Arithmetic

TL;DR

This paper explores a novel streaming binary encoding method and specialized circuits for implementing Boolean and binary operations efficiently on spiking neuromorphic architectures, aiming to enhance on-device processing.

Contribution

It introduces a new streaming binary encoding technique and circuits tailored for spiking neuromorphic systems to perform Boolean and binary operations effectively.

Findings

Proposes a streaming binary encoding method.

Designs circuits for Boolean and binary operations.

Highlights benefits for on-device processing in neuromorphic systems.

Abstract

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.