Combinatorial optimization by weight annealing in memristive hopfield networks.

ABSTRACT: The increasing utility of specialized circuits and growing applications of optimization call for the development of efficient hardware accelerator for solving optimization problems. Hopfield neural network is a promising approach for solving combinatorial optimization problems due to the recent demonstrations of efficient mixed-signal implementation based on emerging non-volatile memory devices. Such mixed-signal accelerators also enable very efficient implementation of various annealing techniques, which are essential for finding optimal solutions. Here we propose a "weight annealing" approach, whose main idea is to ease convergence to the global minima by keeping the network close to its ground state. This is achieved by initially setting all synaptic weights to zero, thus ensuring a qui

SUBMITTER: Fahimi Z 

PROVIDER: S-EPMC8361025 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature