TOKYO – Toshiba Corporation (TOKYO: 6502), the industry leader in solutions for large-scale optimization problems, today announced a scale-out technology that minimizes hardware limitations, an evolution of its optimization computer, the Simulation Bifurcation Machine (SBM), that supports continued increases in computing speed and scale. Toshiba expects the new SBM to be a game changer for real-world problems that require large-scale, high-speed and low-latency, such as simultaneous financial transactions involving large numbers of stock, and complex control of multiple robots. The research results were published in Nature Electronics*1 on March 1.
Speed and scale are keys to success in industrial sectors as different as finance, logistics, and communications, all of which have to deal with large number and make complex decisions in the shortest time possible. Aiming to bring higher efficiencies to these and other businesses, Toshiba has addressed combinatorial optimization problems by developing high-speed, high-accuracy algorithms and corresponding practical computer solutions*2. The company recently announced a second generation of its simulated bifurcation algorithms, implemented on classical computers via a single field programmable gate array (FPGA), that surpasses quantum computers in obtaining optimal solutions for various combinatorial optimization problems at high speed*3.
Toshiba continues to pursue better performance of the SBM by installing more FPGAs in the computer, an approach called scale-out in computer architecture, and has successfully demonstrated the world’s first*4 simultaneous scale-out of computing speed and problem size for all-to-all connection type combinatorial optimization problems*1. At the heart of the technology is a partitioned version of the simulated bifurcation algorithm that enables multiple FPGAs to exchange information on variables with each other, and that triggers an autonomous synchronization mechanism in minimizing the communications overhead to an extent that does not affect overall performance (Figures 1 & 2).