AI News, Massive computational acceleration by using neural networks to ... artificial intelligence


From the world’s largest supercomputers to the vast datacenters that power the cloud, this new computing model is helping to answer complex questions, discover new science, and bring amazing capabilities to our mobile devices.

Alibaba Clouder

The current advanced AI development strategy is deep learning with a learning process divided into two parts: training and inference.

Training usually requires a significant amount of data input, or involves the use of unsupervised learning methods, such as enhanced learning, to create a complex deep neural network model.

Due to the massive training data required and the complicated structures of a deep neural network, the training process requires a vast amount of computation and usually requires GPU clusters to train for several days or even weeks.

Next, on the software side, the chip is integrated with several algorithms developed at the DAMO Academy, which are specifically optimized for convolutional neural network (CNN) and computer vision algorithms, granting the tiny neural processing unit (NPU) the capacity to complete the computing operations of a large neural network.

MaxCompute supports various data importing solutions and distributed computing models, enabling users to effectively query massive datasets, reduce production costs, and ensure data security.

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Accelerating Computation of Deep Neural Networks

livestream the tech talk hosted by SF big analytics meetup group.