Fujitsu's European labs division has revealed a technique to scale deep-learning technologies without major infrastructure spend. The technique distributes models across the memory capacity of an organisation’s range of hardware rather than relying on an array of identical graphics processing units (GPUs), and Fujitsu claims it will make it more cost effective for businesses to apply AI cognition and natural language processing to large data sets. Deep learning, which is based around artificial neural networks that can be trained using massive amounts of data, was first introduced in 2014 by Google as part of its TensorFlow software library.
Fujitsu's European labs division has revealed a technique to scale deep-learning technologies without major infrastructure spend. The technique distributes models across the memory capacity of an organisation’s range of hardware rather than relying on an array of identical graphics processing units (GPUs), and Fujitsu claims it will make it more cost effective for businesses to apply AI cognition and natural language processing to large data sets. The technique creates sub-layers within the neural networks and uses "model-parallelism" to ensure that each sub-layer is functionally equivalent even though it is installed on different chipsets in multiple machines.