<title>Artificial neural network and image processing using the Adaptive Solutions' architecture</title>

Abstract

Adaptive Solutions' CNAPS architecture is a parallel array of digital processors. This design features a Single-Instruction Multiple-Data (SIMD) stream architecture. The architecture is designed to execute on-chip learning for Artificial Neural Network (ANN) algcwithms with unprecedented performance. ANNs have shown impressive results for solving difficult image processing tasks. However, current hardware prevents many ANN solutions from being effective products. The CNAPS architecture will provide the computational power to allow real time ANN applications.Because of the high parallelism of the architecture, it is also ideal for digital image processing tasks. This architecture will allow high performance applications that combine conventional image processing methods and ANNs on the same system. This paper gives a brief introduction to the CNAPS architecture, and gives the system performance on implementations of neural network algorithms, and conventional image processing algorithms such as convolution, and 2D Fourier transforms. INTRODUCTIONArtificial Neural Network classifiers have provided robust solutions to many applications that were previously unsolved problems. There have been impressive results of ANN technology in such areas as optical character recognition, speech recognition, signal processing, and computer vision. However, along with the impressive classifier results, there has been an increased concern for pre-processing required for real-world problems. A major area ofresearch has been concerned with how to best represent input data to the ANN classifiers. Pre-processing of the inputs is especially important for image processing tasks, because of the large input space, inherent noise of image data, and the spatial variations of the target objects. For these reasons, ANN researchers are using conventional image processing techniques for pre-processing the input data.One major impediment to implementation of real time ANN classifier is the large number of computations required to run a typical algorithm. The training and execution time for neural networks takes too long on current single processor systems for real time applications. Execution time is also a problem for image processing systems. The CNAPS architecture, designed by Adaptive Solutions, is a highly parallel solution that will allow many ANN applications to run in real time.1 Although the CNAPS architecwre is designed for running and training ANNs, it can also be used to execute many image processing algonthms in real time. The flexibility of the CNAPS architecture provides a powerful platform for implementing complete image processing applications. This paper will describe the CNAPS architecture, as well as the implementation of ANN and image processing algorithms. Section 2 is an overview of the CNAPS architecture. Section 3 explains the implementation and performance of neural network algorithms. Implementation and performance of image processing algorithms is discussed in Section 4, and Section 5 summarizes the paper. THE CNAPS ARC...