Hardware for neural networks

Neuromorphic computing, rewriting Turing machines for blobby grey matter, other hybrid ideas



Placeholder, for thinking about the implementation and theory of computation as it has been perturbed by our increasing dependence upon neural models for computing.

References

Aimone, James B., and Ojas Parekh. 2023. β€œThe Brain’s Unique Take on Algorithms.” Nature Communications 14 (1): 4910.
Gershenfeld, N. 1996. β€œSignal Entropy and the Thermodynamics of Computation.” IBM Systems Journal 35 (3.4): 577–86.
Gershenfeld, Neil. 2011. β€œAligning the Representation and Reality of Computation with Asynchronous Logic Automata.” Computing 93 (2-4): 91–102.
Gershenfeld, Neil A. 2000. The Physics of Information Technology. Cambridge Series on Information and the Natural Sciences. Cambridge ; New York: Cambridge University Press.
Hooker, Sara. 2020. β€œThe Hardware Lottery.” arXiv:2009.06489 [Cs], September.
Jaeger, Herbert, Beatriz Noheda, and Wilfred G. van der Wiel. 2023. β€œToward a Formal Theory for Computing Machines Made Out of Whatever Physics Offers.” Nature Communications 14 (1): 4911.
Zhu, Ruomin, Sam Lilak, Alon Loeffler, Joseph Lizier, Adam Stieg, James Gimzewski, and Zdenka Kuncic. 2023. β€œOnline Dynamical Learning and Sequence Memory with Neuromorphic Nanowire Networks.” Nature Communications 14 (1): 6697.

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