ACOR: On the Design of Energy-Efficient Autocorrelation for Emerging Edge Applications

Abstract

The identification of patterns and changes in timeseries using the autocorrelation function (ACF) is traditionally used in several applications from communications, multimedia to remote health monitoring. Existing ACF implementations have tried to meet the throughput requirements of specific domains by mainly using time-domain approaches, however such techniques require several costly multiplications, which hinder their use in power-constrained devices, essential in emerging ACF-based edge applications. Frequency-domain (FD-ACF) approaches could reduce the computational complexity of the ACF calculation, but their use is limited in specific domains, leaving room for further power-aware algorithmic and architectural optimizations. This paper presents a framework, named ACOR, for the design of energy-efficient pipelined ACF architectures under various settings, throughput and energy requirements that vary across ACF-based applications. The proposed framework allows the quick exploration of ACF architectures for different sampling window sizes, window overlapping ratios, number of lags, and precision levels, which is impossible with the existing scattered domain-specific works. Our experimental results show that when compared with existing ACF architectures used in bio-signal analysis, linear predictive coding and telecommunications our proposed framework achieves up to 27.18%, and 51.47% reduction in the circuit area and energy consumption, respectively, with a slight throughput reduction of 8%.