This paper introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimize energy consumption. In this scenario, the reliability of SRAM memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, we propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e., their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. We illustrate our scheme on an industrial benchmark application performing the power spectrum analysis (PSA) of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.
Devis Tuia, Diego Michael Schibli