A coalition of academic and industry researchers argues that chips exploiting natural physical dynamics, rather than enforcing digital abstractions, could dramatically cut AI computing costs.
Matrix Multiplication in Quadratic Time and Energy? Towards a Fine-Grained Energy-Centric Church-Turing Thesis
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We describe two algorithms for multiplying n x n matrices using time and energy n^2 polylog(n) under basic models of classical physics. The first algorithm is for multiplying integer-valued matrices, and the second, quite different algorithm, is for Boolean matrix multiplication. We hope this work inspires a deeper consideration of physically plausible/realizable models of computing that might allow for algorithms which improve upon the runtimes and energy usages suggested by the parallel RAM model in which each operation requires one unit of time and one unit of energy.
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Solving the compute crisis with physics-based ASICs
A coalition of academic and industry researchers argues that chips exploiting natural physical dynamics, rather than enforcing digital abstractions, could dramatically cut AI computing costs.