Advancing System Performance with Redundancy: From Biological to Artificial Designs

TL;DR

This paper proposes a unified theory of redundancy inspired by biological systems, demonstrating how redundancy can enhance accuracy and performance in both biological and artificial systems through representational and entangled mechanisms.

Contribution

It introduces a new theoretical framework explaining how redundancy improves system performance beyond fault tolerance, applicable to biological and artificial systems.

Findings

Redundancy can be engineered for accuracy and precision, not just fault tolerance.

The theory applies to biological systems like the human musculoskeletal system.

It also explains the performance of artificial systems like ResNet.

Abstract

Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular and nervous system; yet its function has not been fully understood. The conventional interpretation of redundancy is that it serves as a fault-tolerance mechanism, which leads to redundancy's de facto application in man-made systems for reliability enhancement. On the contrary, our previous works have demonstrated an example where redundancy can be engineered solely for enhancing other aspects of the system, namely accuracy and precision. This design was inspired by the binocular structure of the human vision which we believe may share a similar operation. In this paper, we present a unified theory describing how such utilization of redundancy is feasible through two complementary mechanisms: representational redundancy (RPR) and entangled redundancy (ETR). Besides the previous works, we point out two additional examples where our new understanding of redundancy can be applied to justify a system's superior performance. One is the human musculoskeletal system (HMS) - a biological instance, and one is the deep residual neural network (ResNet) - an artificial counterpart. We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems as well as assist the studies of the fundamental mechanisms governing various biological processes.