Amorphica: 4-Replica 512 Fully Connected Spin 336MHz Metamorphic Annealer with Programmable Optimization Strategy and Compressed-Spin-Transfer Multi-Chip Extension

Combinatorial optimization (CO) is vital for making wiser decisions and planning in our society. Annealing computation is a promising CO approach derived from an analogy to physical phenomena (Fig. 2.3.1). It represents a CO problem as an energy function, a quadratic form of {1, -1} vectors, where e...

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Bibliographic Details
Main Authors: Kawamura, Kazushi, Yu, Jaehoon, Okonogi, Daiki, Jimbo, Satoru, Inoue, Genta, Hyodo, Akira, Lopez Garcia-Arias, Angel, Ando, Kota, Fukushima-Kimura, Bruno Hideki, Yasudo, Ryota, Van Chu, Thiem, Motomura, Masato
Format: Conference Proceeding
Language:English
Subjects:
Annealing
Couplings
Energy states
Optimization
Planning
Quantum annealing
Topology
Online Access:Request full text
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Summary:Combinatorial optimization (CO) is vital for making wiser decisions and planning in our society. Annealing computation is a promising CO approach derived from an analogy to physical phenomena (Fig. 2.3.1). It represents a CO problem as an energy function, a quadratic form of {1, -1} vectors, where each binary element is called a (pseudo) spin. The spin vector is initialized randomly and is updated stochastically to find minimum energy states by gradually reducing the (pseudo) temperature. Local-connection annealers (quantum [1] and non-quantum [2-4]) have been constrained to spin models having only local inter-spin couplings. This restriction, however, severely limits their CO applications even with the help of clever graph embedding algorithms. Full-connection annealers [5], [6], considered here, have been proposed to address this drawback, permitting handling of arbitrary topologies and densities of inter-spin couplings, even if they are irregular.
ISSN:2376-8606
DOI:10.1109/ISSCC42615.2023.10067504