TY - GEN
T1 - A low-power microprocessor for data-driven analysis of analytically- intractable physiological signals in advanced medical sensors
AU - Lee, Kyong Ho
AU - Verma, Naveen
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84883770348
SN - 9784863483484
T3 - IEEE Symposium on VLSI Circuits, Digest of Technical Papers
SP - C250-C251
BT - 2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers
T2 - 2013 Symposium on VLSI Circuits, VLSIC 2013
Y2 - 12 June 2013 through 14 June 2013
ER -