A Parallel Processor Chip for Image Processing and Neural Networks

At AT&T Bell Laboratories we have developed two neural-network chips: (i) the NET32K chip [1] with 32,768 1-bit processing elements which is well suited for image-analysis tasks such as feature extraction and (ii) the ANNA chip [2] with 4,096 3/6-bit processing elements designed for high-speed character recognition using our neural-net based optical character recognition (OCR) algorithms [3, 4]). Board systems for both chips have been built and are used in prototype systems. A new chip, called HIP for Homogeneous Image Processor, is now under development. This chip leverages the experience gained with the earlier chips, but is targeted at a larger range of applications. Besides neural networks, image analysis, and OCR, additional applications such as video coding/decoding, speech recognition, and graphics can take advantage of the new chip's huge computational power of 25 billion operations per second. The high exibility and the large set of applications can potentially lead to large chip volumes thereby enabling a reduced cost per chip commercially.