DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems
Abstract: With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamical systems fabricated with CMOS technologies, have emerged as a new approach that harnesses the inherent power of nature within dynamical systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees generality for optimization problems poses daunting scalability concerns.To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamical system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamical system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and community structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamical system while maintaining high accuracy. Evaluations with four diverse GL applications across seven real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 10 3 × to 10 5 × speedups over Graph Neural Networks on GPUs while operating at a power 2 orders of magnitude lower than GPUs, with 5% – 30% accuracy enhancement.
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