The connection machine
Scan line array processors for image computation
ISCA '86 Proceedings of the 13th annual international symposium on Computer architecture
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
The AIS-5000 Parallel Processor
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The Massively Parallel Processor
The Massively Parallel Processor
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Parallel processors offer a very attractive mechanism for the implementation of large neural networks. Problems in the usage of parallel processing in neural computing involve the difficulty of handling the large amount of global communication between processing units and the storage of weights for each of the neural processor connections. This paper will discuss how massive parallelism in the form of a one dimensional SIMD array can handle indefinitely large networks in near real time by efficiently organizing the memory storage of weights, and input and output signals. Very little overhead time is used in communication of signals between processing units, and there is no idle time for any of the units. An advantage of SIMD array systems is that the arithmetic processing is done bit serially, with the result that trade-offs can be easily be made between the processor speed and precision of the signals and weights.