Systolic array implementation of artificial neural networks

This paper describes systolic implementation schemes for Hopfield and Hamming nets using completely digital circuits. In the proposed architecture, input data are passed through the neurons on a time share basis, weights are stored in digital shift registers and no separate threshold detectors are used. The architecture provides massive parallelism, reprogrammability and can be expanded by cascading identical chips. © 1994.