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Creators/Authors contains: "Stan, Mircea"

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  1. ; ; ; ; ; (, Physical Review Applied)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; ; (, IEEE)
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  3. ; ; ; ; ; (, IEEE)
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  4. ; ; ; ; ; (, Journal of Low Power Electronics and Applications)
    Energy-efficient microprocessors are essential for a wide range of applications. While near-threshold computing is a promising technique to improve energy efficiency, optimal supply demands from logic core and on-chip memory are conflicting. In this paper, we perform static reliability analysis of 6T SRAM and discover the variance among different sizing configuration and asymmetric minimum voltage requirements between read and write operations. We leverage this asymmetric property i n near-threshold processors equipped with voltage boosting capability by proposing an opportunistic dual-supply switching scheme with a write aggregation buffer. Our results show that proposed technique improves energy efficiency by more than 21.45% with approximate 10.19% performance speed-up. 
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  5. ; ; ; ; (, Design Automation Conference)
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  6. ; ; ; ; ; (, Image Sensing Technologies: Materials, Devices, Systems, and Applications V)
    In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras. 
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