Towards hardware-driven design of low-energy algorithms for data analysis - Indre Zliobaite; Jaakko Hollmen; Jukka Teittinen; Lauri Koskinen (A C M Special Interest Group, 2022)
In the era of "big" data, data analysis algorithms need to be efficient. Traditionally researchers would tackle this problem by considering "small" data algorithms, and investigating how to make them computationally more efficient for big data applications. The main means to achieve computational efficiency would be to revise the necessity and order of subroutines, or to approximate calculations. This paper presents a viewpoint that in order to be able to cope with the new challenges of the growing digital universe, research needs to take a combined view towards data analysis algorithm design and hardware design, and discusses a potential research direction in taking an intreated approach in terms of algorithm design and hardware design. Analyzing how data mining algorithms operate at the elementary operations level can help do design more specialized and dedicated hardware, that, for instance, would be more energy efficient. In turn, understanding hardware design can help to develop more effective algorithms.