A key challenge in closed-loop chronic biomedical systems is the ability to detect complex physiological states from patient signals within a constrained power budget. Data-driven machine-learning techniques are major enablers for the modeling and interpretation of such states. Their computational energy, however, scales with the complexity of the required models. In this paper, we propose a low-energy, biomedical computation platform optimized through the use of an accelerator for data-driven classification. The accelerator retains selective flexibility through hardware reconfiguration and exploits voltage scaling and parallelism to operate at a sub-threshold minimum-energy point. Using cardiac arrhythmia detection algorithms with patient data from the MIT-BIH database, classification is achieved in 2.96 \u03bcJ (at Vdd = 0.4 V), over four orders of magnitude smaller than that on a low-power general-purpose processor. The energy of feature extraction is 148 \u03bcJ while retaining flexibility for a range of possible biomarkers.<\/p>\n","protected":false},"excerpt":{"rendered":"
A key challenge in closed-loop chronic biomedical systems is the ability to detect complex physiological states from patient signals within a constrained power budget. Data-driven machine-learning techniques are major enablers for the modeling and interpretation of such states. Their computational energy, however, scales with the complexity of the required models. 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