Dynamic Range Reduction via Branch-and-Bound

• URL: http://arxiv.org/abs/2409.10863v1
• Date: Tue, 17 Sep 2024 03:07:56 GMT
• Title: Dynamic Range Reduction via Branch-and-Bound
• Authors: Thore Gerlach, Nico Piatkowski,
• Abstract summary: Key strategy to enhance hardware accelerators is the reduction of precision in arithmetic operations. This paper introduces a fully principled Branch-and-Bound algorithm for reducing precision needs in QUBO problems. Experiments validate our algorithm's effectiveness on an actual quantum annealer.
• Score: 1.533133219129073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The demand for high-performance computing in machine learning and artificial intelligence has led to the development of specialized hardware accelerators like Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), and Field-Programmable Gate Arrays (FPGAs). A key strategy to enhance these accelerators is the reduction of precision in arithmetic operations, which increases processing speed and lowers latency - crucial for real-time AI applications. Precision reduction minimizes memory bandwidth requirements and energy consumption, essential for large-scale and mobile deployments, and increases throughput by enabling more parallel operations per cycle, maximizing hardware resource utilization. This strategy is equally vital for solving NP-hard quadratic unconstrained binary optimization (QUBO) problems common in machine learning, which often require high precision for accurate representation. Special hardware solvers, such as quantum annealers, benefit significantly from precision reduction. This paper introduces a fully principled Branch-and-Bound algorithm for reducing precision needs in QUBO problems by utilizing dynamic range as a measure of complexity. Experiments validate our algorithm's effectiveness on an actual quantum annealer.

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