A low-power microprocessor for data-driven analysis of analytically-intractable physiological signals in advanced medical sensors

Data-driven methods based on machine learning enable powerful frameworks for analyzing complex physiological signals in medical-sensor applications; however, these methods are not well supported by traditional DSPs. A general-purpose microprocessor is presented in 130nm CMOS that integrates configurable accelerators, enabling low-energy hardware to support the broadest range of machine-learning frameworks reported to date. In addition to computational energy, memory limitations due to the high-order data-driven models are overcome by an embedded compression/decompression accelerator, which reduces the memory footprint by 4× with overhead <;8%. Using six medical applications with real clinical data, overall energy savings of 3.1-497× are demonstrated with the accelerator-based architecture.