by Professor Mohamed M. Sabry Aly,
Nanyang Technological University, Singapore
As computing devices nowadays become ever more multi-functional with a wide plethora of features, the need for an energy-efficient computing system has become increasingly crucial. This talk gives an overview of deep learning at the edge for ultra-low power AI systems, where the entire development stack, from algorithms to hardware devices, was optimised. The main goal of this approach is to enable the execution of deep learning applications on ultra-low power edge platforms, while conserving the battery lifetime. This approach achieves significant gains over current solutions, reaching 10x savings on total system power.