Application of the ANNA neural network chip to high-speed character recognition

A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precisio

An analog neural network processor and its application to high-speed character recognition

A high-speed programmable neural network chip and its application to character recognition are described. A network with over 130,000 connections has been implemented on a single chip and operates at a rate of over 1000 classifications per second. The chip performs up to 2000 multiplications and additions simultaneously. Its datapath is suitable particularly for the convolutional architectures that are typical in pattern classification networks, but can also be configured for fully connected or feedback topologies. Computations were performed with 6 bits accuracy for the weights and 3 bits for the states. The chip uses analog processing internally for higher density and reduced power dissipation, but all input/output is digital to simplify system integratio

An analog neural network processor with programmable topology

The architecture, implementation, and applications of a special-purpose neural network processor are described. The chip performs over 2000 multiplications and additions simultaneously. Its data path is particularly suitable for the convolutional topologies that are typical in classification networks, but can also be configured for fully connected or feedback topologies. Resources can be multiplexed to permit implementation of networks with several hundreds of thousands of connections on a single chip. Computations are performed with 6 b accuracy for the weights and 3 b for the neuron states. Analog processing is used internally for reduced power dissipation and higher density, but all input/output is digital to simplify system integration. The practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition. This network contains over 130000 connections and was evaluated in 1 m

A neural network approach to handprint character recognition

The authors outline OCR (optical character recognition) technology developed at AT&T Bell Laboratories, including a recognition network that learns feature extraction kernels and a custom VLSI chip that is designed for neural-net image processing. It is concluded that both high speed and high accuracy can be obtained using neural-net methods for character recognition. Networks can be designed that learn their own feature extraction kernels. Special-purpose neural-net chips combined with digital signal processors can quickly evaluate character-recognition neural nets. This high speed is particularly useful for recognition-based segmentation of character strings

Metrics and Models for Handwritten Character Recognition

A digitized handwritten numeral can be represented as a binary or greyscale image. An important pattern recognition task that has received much attention lately is to automatically determine the digit, given the image. While many different techniques have been pushed very hard to solve this task, the most successful and intuitively appropriate is due to Simard (Simard, LeCun & Denker 1993). Their approachcombined nearest-neighbor classification with a subjectspecific invariant metric that allows for small rotations, translations, and other natural transformations. We report on Simard's classifier, and compare it to other approaches. One important negative aspect of near-neighbor classification is that all the work gets done at lookup time, and with around 10,000 training images in high dimensions this can be exorbitant. In this paper we develop rich models for representing large subsets of the prototypes. One example is a low-dimensional hyperplane defined byapoint and a se..