CAREER: iMPACT: Metaphysical and Probabilistic-Based Computing Transformation with Emerging Spin-Transfer Torque Device Technology
CAREER: iMPACT: Metaphysical and Probabilistic-Based Computing Transformation with Emerging Spin-Transfer Torque Device Technology
批准号:
1553056
负责人:
Mingjie Lin
金额:
$54.13万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-05-01 至 2023-04-30
中文摘要
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英文摘要
Effectively tackling the upcoming "zettabyte" data explosion requires a quantum leap in computing power and energy efficiency. However, with the Moore's law dwindling quickly, the physical limits of CMOS technology make it almost impossible to achieve high energy efficiency if the traditional "deterministic and precise" computing model still dominates. This CAREER proposal is inspired by applications in brain-like computing and aims at developing an alternative, non-Boolean, non-CMOS computing paradigm capable of overcoming the limitations in computational efficiency of digital CMOS technology posed by quantum-related device physics. This research, if successful, will offer a solution to tackle the upcoming "zettabyte" data explosion. In addition, the PI approaches the challenge of broadening participation from underrepresented minority groups in both bottom-up (public STEM education) and top-down (PhD students recruiting) manner. In particular, the PI and his team will use Orlando Science Center as the main platform to stimulate public interests through innovative exhibits and mini-lectures as well as engage in STEM education in the context of computing. This CAREER proposal revolves around the Metaphysical and Probabilistic Computing Transformation, an innovative paradigm that transcends deterministic computing by natively exploiting randomness-driven physical phenomenon, either from CMOS devices under extreme conditions or from emerging spin-torque devices. The ultimate goal is to achieve, for a wide-range of perception-based computing applications - and thus for applications where the human Brain performs better than current machines - more than 10 times improvement in computing performance, more than 100 times in energy efficiency, and more than 10 times in hardware robustness over the existing state-of-the-art.
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依托单位:
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