Justin Wong

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2018-187

December 17, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-187.pdf

Properties that emerge from the collective behavior of constituents at different length scales can be exploited to reduce power consumption below conventional limits in computing. At the device level, ferroelectric-dielectric coupling ("negative capacitance") can reduce energy consumption below 1 / 2 CV 2 in capacitors. However, this effect is still not well understood. We construct a microscopic model and analyze energy flow from the perspective of Poynting's theorem to clear up these misunderstandings. At the circuit level, high-dimensional distributed representations relax requirements on signal-to-noise ratio and supply voltage, and enable new architecture designs. Computing with these representations ("hyperdimensional computing") is natural for performing energy efficient cognitive computing at the application level. However, data in practice is always measured in some sort of representation, which may not be natural for hyperdimensional computing. We bridge this gap by proposing to use an approximation of the bispectrum to map data measured in practice into high-dimensional distributed representations for use with hyperdimensional computing.

Advisors: Sayeef Salahuddin

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BibTeX citation:

@phdthesis{Wong:EECS-2018-187,
    Author= {Wong, Justin},
    Title= {Negative capacitance and hyperdimensional computing for unconventional low-power computing},
    School= {EECS Department, University of California, Berkeley},
    Year= {2018},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-187.html},
    Number= {UCB/EECS-2018-187},
    Abstract= {Properties that emerge from the collective behavior of constituents at different length scales can be exploited to reduce power consumption below conventional limits in computing. At the device level, ferroelectric-dielectric coupling ("negative capacitance") can reduce energy consumption below 1 / 2 CV 2 in capacitors. However, this effect is still not well understood. We construct a microscopic model and analyze energy flow from the perspective of Poynting's theorem to clear up these misunderstandings. At the circuit level, high-dimensional distributed representations relax requirements on signal-to-noise ratio and supply voltage, and enable new architecture designs. Computing with these representations
("hyperdimensional computing") is natural for performing energy efficient cognitive computing at the application level. However, data in practice is always measured in some sort of representation, which may not be natural for hyperdimensional computing. We bridge this gap by proposing to use an approximation of the bispectrum to map data measured in practice into high-dimensional distributed representations for use with hyperdimensional computing.},
}

EndNote citation:

%0 Thesis
%A Wong, Justin 
%T Negative capacitance and hyperdimensional computing for unconventional low-power computing
%I EECS Department, University of California, Berkeley
%D 2018
%8 December 17
%@ UCB/EECS-2018-187
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-187.html
%F Wong:EECS-2018-187