Oscillatory Neural Network as Hetero-Associative Memory for Image Edge Detection

by   Madeleine Abernot, et al.

The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory Neural Networks (ONN) present a novel brain-inspired computing approach by emulating brain oscillations to perform autoassociative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel image processing method by using ONNs as a hetero-associative memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time image processing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for image processing applications.


page 2

page 7


Cryogenic Neuromorphic Hardware

The revolution in artificial intelligence (AI) brings up an enormous sto...

Dilated filters for edge detection algorithms

Edges are a basic and fundamental feature in image processing, that are ...

Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

Neuromodulation techniques have emerged as promising approaches for trea...

An efficient circle detection scheme in digital images using ant system algorithm

Detection of geometric features in digital images is an important exerci...

PISA: A Binary-Weight Processing-In-Sensor Accelerator for Edge Image Processing

This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a...

PhiNets: a scalable backbone for low-power AI at the edge

In the Internet of Things era, where we see many interconnected and hete...

Please sign up or login with your details

Forgot password? Click here to reset