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Fujitsu Doubles Deep Learning Neural Network Scale

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fuby Angela Guess

According to a recent press release out of the company, “Fujitsu Laboratories Ltd. today announced development of technology to streamline the internal memory of GPUs to support the growing neural network scale that works to heighten machine learning accuracy. This development has enabled neural network machine learning of a scale up to twice what was capable with previous technology. Recent years have seen a focus on technologies that use GPUs for high-speed machine learning to support the huge volume of calculations necessary for deep learning processing. In order to make use of a GPU’s high-speed calculation ability, the data to be used in a series of calculations needs to be stored in the GPU’s internal memory. This, however, creates an issue where the scale of the neural network that could be built is limited by memory capacity.”

The release continues, “Fujitsu Laboratories has now developed technology to improve memory efficiency, implementing and evaluating it in the Caffe open source deep learning framework software. Upon commencement of learning, the technology analyzes the structure of the neural network, and optimizes the order of calculations and allocation of data to memory, so that memory space can be efficiently reused. With AlexNet and VGGNet(1), image-recognition neural networks widely used in research, this technology was confirmed to enable the scale of learning of a neural network to be increased by up to roughly two times that of previous technology, thereby reducing the volume of internal GPU memory used by over 40%. This technology makes it possible to expand the scale of a neural network that can be learned at high speed on one GPU, enabling the development of more accurate models. Fujitsu Laboratories aims to commercialize this technology as part of Fujitsu Limited’s AI technology, Human Centric AI Zinrai, to work with customers in the use of AI.”

Read more at ACN Newswire.

Photo credit: Fujitsu

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