Energy-Efficient Time Series Analysis Using Transprecision Computing

Abstract

Time series analysis is a key step in monitoring and predicting events over time in domains such as epidemiology, genomics, medicine, seismology, speech recognition, and economics. Matrix Profile has been recently proposed as a promising technique to perform time series analysis. For each subsequence, the matrix profile provides the most similar neighbour in the time series. This computation requires a huge amount of floating-point (FP) operations, which are a major contributor (approximately 50%) to the energy consumption in modern computing platforms. Transprecision Computing has recently emerged as a promising approach to improve energy efficiency and performance by tolerating some loss of precision in FP operations. In this work, we study how the matrix profile parallel algorithms benefit from transprecision computing using a recently proposed transprecision FPU. This FPU is intended to be integrated on embedded devices as part of RISC-V processors, FPGAs or ASICs to perform energy-efficient time series analysis. To this end, we propose an accuracy metric to compare the results with the double precision matrix profile. We use this metric to explore a wide range of exponent and mantissa combinations for a variety of datasets, as well as a mixed precision and a vectorized approach. Our analysis reveals that the energy consumption is reduced up to 3.3x compared with double precision approaches, while only slightly affecting the accuracy.