no code implementations • 3 Feb 2020 • Paul Wood, Hossein Pourmeidani, Ronald F. DeMara
Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing devices are garnering increasing interest as a means to compactly and efficiently realize machine learning operations in Restricted Boltzmann Machines (RBMs).
no code implementations • 16 Apr 2019 • Arman Roohi, Shaahin Angizi, Deliang Fan, Ronald F. DeMara
Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelerator is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) computational sub-arrays.
no code implementations • 8 Jan 2019 • Ramtin Zand, Ronald F. DeMara
In this paper, a spintronic neuromorphic reconfigurable Array (SNRA) is developed to fuse together power-efficient probabilistic and in-field programmable deterministic computing during both training and evaluation phases of restricted Boltzmann machines (RBMs).
no code implementations • 28 Nov 2018 • Ramtin Zand, Kerem Y. Camsari, Supriyo Datta, Ronald F. DeMara
Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restricted Boltzmann machines (RBMs) to form deep belief networks (DBNs).