Temporal Computer Organization

TL;DR

This paper explores the design of temporal computing systems, especially spiking neural networks, using synchronized segments and clocks to achieve biologically plausible and functionally complete temporal processing.

Contribution

It introduces a system organization with synchronized segments and a flexible clock mechanism to enhance temporal neural network capabilities.

Findings

Proposes a segmented system architecture for temporal computation.

Introduces a clock-based synchronization method for neural networks.

Enhances biological plausibility and functional completeness.

Abstract

This document is focused on computing systems implemented in technologies that communicate and compute with temporal transients. Although described in general terms, implementations of spiking neural networks are of primary interest. As background, an algebra for constructing temporal networks is summarized. Then, a system organization consisting of synchronized segments is described. The segments are feedforward internally with feedback between segments. A synchronizing clock resets network segments at the end of each computation step or cycle. In its basic form, the synchronizing clock merely performs a reset function. In the context of neural networks, this satisfies biological plausibility. However, functional completeness is restricted. This restriction is removed by allowing use of the synchronizing clock as an additional function input that acts as a temporal reference value.