Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions

• URL: http://arxiv.org/abs/2501.00015v1
• Date: Sat, 14 Dec 2024 23:24:28 GMT
• Title: Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions
• Authors: Nicolas Alder, Shivam Nitin Kajale, Milin Tunsiricharoengul, Deblina Sarkar, Ralf Herbrich,
• Abstract summary: We introduce an energy-efficient algorithm for uniform Float16 sampling using a room-temperature magnetic tunnel junction device.<n>We decompose arbitrary distributions into many non-overlapping approximative uniform distributions along with convolution and prior-likelihood operations.
• Score: 0.6990493129893112
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: (Pseudo)random sampling, a costly yet widely used method in (probabilistic) machine learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large scale due to unmet computational requirements. We introduce an energy-efficient algorithm for uniform Float16 sampling, utilizing a room-temperature stochastic magnetic tunnel junction device to generate truly random floating-point numbers. By avoiding expensive symbolic computation and mapping physical phenomena directly to the statistical properties of the floating-point format and uniform distribution, our approach achieves a higher level of energy efficiency than the state-of-the-art Mersenne-Twister algorithm by a minimum factor of 9721 and an improvement factor of 5649 compared to the more energy-efficient PCG algorithm. Building on this sampling technique and hardware framework, we decompose arbitrary distributions into many non-overlapping approximative uniform distributions along with convolution and prior-likelihood operations, which allows us to sample from any 1D distribution without closed-form solutions. We provide measurements of the potential accumulated approximation errors, demonstrating the effectiveness of our method.

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