Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency

• Main Authors: Niklas Karvonen, Lara Lorna Jimenez, Miguel Gomez Simon, Joakim Nilsson, Basel Kikhia, Josef Hallberg
• Format: Article
• Language: English
• Published: Atlantis Press 2017-01-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Classifier; Energy-saving; Parallel computing; FPGA; Microcontroller; Embedded
• Online Access: https://www.atlantis-press.com/article/25883367/view

Computational intelligence is often used in smart environment applications in order to determine a user’s context. Many computational intelligence algorithms are complex and resource-consuming which can be problematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. These types of devices are, however, highly useful in pervasive and mobile computing due to their small size, energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classifier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers. CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing. The classifier was evaluated on eight different datasets of various types. Our results show that CORPSE, despite its simplistic design, has comparable performance to some common machine learning algorithms. This makes the classifier a viable choice for use in pervasive systems that have limited resources, requires energy-efficiency, or have the need for fast real-time responses.