Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.

ABSTRACT: The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dyna

SUBMITTER: Mahmoodi MR 

PROVIDER: S-EPMC6841978 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature