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When it comes to computing, size matters. Hardware dimensions typically line up with computers’ capabilities, with big tasks in artificial intelligence (AI) such as machine learning algorithms usually needing more extensive and bulky computing servers. However, emerging technologies could one day put supercomputing in the palms of our hands; specifically, through our edge devices.

Edge devices demand low energy consumption, cost, and small form factor. To efficiently deploy convolutional neural network (CNN) models on the edge device, energy-aware model compression becomes extremely important.

In this research, A*STAR's Institute of High Performance Computing (IHPC) researchers propose EDCompress (EDC), an energy-aware model compression method for various dataflows that could effectively reduce the energy consumption of various edge devices, with different dataflow types. "EDCompress also contributes to a ‘green AI’ future, driving down carbon emissions associated with advanced computing," said Zhehui Wang, a Research Scientist from the Computing & Intelligence Department.