Implementation of a binary neural network on a passive array of magnetic tunnel junctions (Conference Presentation)
Jonathan M. Goodwill, Nitin Prasad, Brian D. Hoskins, Matthew W. Daniels, Advait Madhavan, Lei Wan, Tiffany Santos, Michaël Tran, J. A. Katine, P. M. Braganca, Mark D. Stiles, Jabez J. McClelland · 2022
Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, nonvolatility and scalability. In a hardware realization, however, device variations, write errors, and parasitic resistance will generally degrade performance. To quantify such effects, we perform experiments on a 2-layer perceptron constructed from a 15 × 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve accuracy of up to 95.3 % with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.