# Cost-Performance Tradeoffs in Fusing Unreliable Computational Units

## TL;DR

This paper analyzes how to optimally fuse unreliable computational units to minimize cost while achieving a desired error performance, revealing different strategies based on cost functions.

## Contribution

It provides a theoretical framework for understanding cost-performance tradeoffs in fusing unreliable units, with insights applicable to neuroscience, circuits, and crowdsourcing.

## Key findings

- Optimal strategies vary with cost function shape.
- Fusing multiple units is beneficial under convex costs.
- Single reliable units are preferable under linear or concave costs.

## Abstract

We investigate fusing several unreliable computational units that perform the same task. We model an unreliable computational outcome as an additive perturbation to its error-free result in terms of its fidelity and cost. We analyze performance of repetition-based strategies that distribute cost across several unreliable units and fuse their outcomes. When the cost is a convex function of fidelity, the optimal repetition-based strategy in terms of incurred cost while achieving a target mean-square error (MSE) performance may fuse several computational units. For concave and linear costs, a single more reliable unit incurs lower cost compared to fusion of several lower cost and less reliable units while achieving the same MSE performance. We show how our results give insight into problems from theoretical neuroscience, circuits, and crowdsourcing.