How can quantization solve some modern Deep Learning issues?

Emilio Paolini, our affiliated PhD candidate, was invited to the joint Abdus Salam International Centre for Theoretical Physics (ICTP)International Atomic Energy Agency (IAEA) School on FPGA-based Systems-on-Chip and its Applications to Nuclear and Scientific Instrumentation. 

On November 16th, he gave a lecture about quantization in neural networks, focusing on the advantages and the limitations that this technique can introduce. The lecture included a hands-on session, showing how to implement quantization and the impact on the performance of actual neural networks. 

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