Machine Learning Learns to Share the Load


Currently, data is everything and almost every business is in the digital age. So it won’t be wrong to say that we are in a digital age. Today the rabbit hole has a link to information that can not only save time but perform critical tasks. There is so much data available that it is virtually impossible to link them all together. Due to this, data-driven technologies such as machine learning can also come under stress and result in loss of computing power. When putting together the data can pose a serious challenge leading to a deficiency in the performance of the computers. Machine learning can skip recognizing the patterns behind the bulk data.

PNNL’s team of researchers believe that they have made a breakthrough in solving this issue. The team’s concept is based on an innovative approach to machine learning. This approach -the graph convolution networks a.k.a GCN. This approach infers the data that is present in the form of graphs. To perform this task, the scientists have developed new hardware that can accelerate the performance of a machine learning algorithm by automatically adjust or auto-tune the load on the machines. By spreading the workload equally on parallel machines, can enhance the performance of the algorithm.

What was the Need of New Models?

Scientists state that the amount of data that generates today too massive for technologies such as big data to handle. Due to this excessive data, the performance of entire system gets hampered and results in the poor quality of results. Hence it was essential to develop a new system that can shed the load from a single machine and distribute it along with the network of hardware. This brainstorming result can elevate the graph of device and system performance with passing time.


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