Classification on Embedded Systems
Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true, for example, for the fields of sports, automotive, and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources.
With the Embedded Classification Software Toolbox (ECST), we present a solution to the two main challenges, namely obtaining a classification system with low computational complexity and, at the same time, high classification accuracy. For the first challenge, we propose complexity measures on the mathematical operation and parameter level, because the abstraction level of the commonly used Landau notation is too high in the context of embedded system implementation. For the second challenge, we present a software toolbox that trains different classification systems, compares their classification accuracy, and finally analyzes the complexity of the trained system.
The source code is available at GitHub
Approaching the accuracy-cost conflict in embedded classification system design
In: Pattern Analysis and Applications 19 (2016), p. 839-855
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Software-based Performance and Complexity Analysis for the Design of Embedded Classification Systems
21st International Conference on Pattern Recognition (ICPR 2012) (Tsukuba, 11. November 2012 - 15. November 2012)
In: IEEE (ed.): Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR) 2012
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