Abstract:Abstract-Computing with large die-size graphical processors (that need huge arrays of identical structures) in the late CMOS era is abounding with challenges due to spatial non-idealities arising from chip-to-chip and within-chip variation of MOSFET threshold voltage. In this paper, we propose a machine learning based software-framework for in-situ prediction and correction of computation corrupted due to threshold voltage variation of transistors. Based on semi-supervised training imparted to a fully connecte… Show more
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