Simulation and implementation of two-layer oscillatory neural networks for image edge detection: bidirectional and feedforward architectures

Abstract The growing number of edge devices in everyday life generates a considerable amount data that current AI algorithms, like artificial neural networks, cannot handle inside with limited bandwidth, memory, and energy available. Neuromorphic computing, low-power oscillatory networks (ONNs), is an alternative attractive solution to solve complex problems at the edge. However, ONN currently its fully-connected recurrent architecture auto-associative memory problems. In this work, we use two-layer bidirectional architecture. We introduce feedforward perform image detection, using replace convolutional filters scan image. Using HNN Matlab emulator digital design simulations, report efficient detection from both architectures various size (3 × 3, 5 5, 7 7) on black white images. contrast, can also gray scale With design, assess latency performances obtain 3 filter real-time (camera flow 25 30 images per second) up 128 pixels while same deal 170 pixels, due faster computation.