Cost-Reliability Tradeoffs in Fusing Unreliable Computational Units
Cost-Reliability Tradeoffs in Fusing Unreliable Computational Units
复制标题
融合不可靠计算单元的成本与可靠性权衡
DOI：
10.1109/ojsp.2020.2997262
复制
发表时间：
2020
影响因子：
2.8
通讯作者：
L. Varshney
中科院分区：
文献类型：
--
作者：
Mehmet A. Donmez;M. Raginsky;A. Singer;L. Varshney
我们研究融合几个不可靠的计算单元，执行相同的任务。我们将一个不可靠的计算结果建模为对其无误差结果的保真度和成本的加性扰动。我们分析了基于复制的策略的可靠性，这些策略将成本分配给几个不可靠的单元并融合它们的结果。当成本是保真度的凸函数时，在实现目标均方误差水平的同时，在所引起的成本方面的最优基于复制的策略可能会融合几个不可靠的计算单元。对于凹型和线性成本，单个更可靠的单元与多个较低成本和不太可靠的单元的融合相比产生较低的成本，同时实现相同的均方误差水平。我们展示了我们的研究结果如何从理论神经科学和电路中洞察问题。
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 reliability of replication-based strategies that distribute cost across several unreliable units and fuse their outcomes. When the cost is a convex function of fidelity, the optimal replication-based strategy in terms of incurred cost while achieving a target mean-square error level may fuse several unreliable 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 mean-square error level. We show how our results give insight into problems from theoretical neuroscience and circuits.