Amorphica: 4-Replica 512 Fully Connected Spin 336MHz Metamorphic Annealer with Programmable Optimization Strategy and Compressed-Spin-Transfer Multi-Chip Extension
Kazushi Kawamura, Jaehoon Yu, Daiki Okonogi, Satoru Jimbo, G. Inoué, Akira Hyodo, Ángel López García-Arias, Kota Ando, Bruno Hideki Fukushima-Kimura, Ryota Yasudo, Thiem Van Chu, Masato Motomura
Abstract
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.