Optimization problems with low SWaP tactical Computing
This addresses resource-constrained computing for tactical applications, but it is incremental as it focuses on known trade-offs without introducing new methods.
The paper tackles the problem of optimizing computational strategies under size, weight, and power (SWaP) constraints in tactical environments, resulting in the identification that only simple, fast algorithms are feasible for adaptive computing.
In a resource-constrained, contested environment, computing resources need to be aware of possible size, weight, and power (SWaP) restrictions. SWaP-aware computational efficiency depends upon optimization of computational resources and intelligent time versus efficiency tradeoffs in decision making. In this paper we address the complexity of various optimization strategies related to low SWaP computing. Due to these restrictions, only a small subset of less complicated and fast computable algorithms can be used for tactical, adaptive computing.