• image recognition
• computer vision
• neural networks
• posit

In last few years, deep neural networks (DNNs) have provided numerous successes inseveral application domains. The main issues that affected this progress have been the huge resource requirements of neural network training.In order to face this problem, a lot of work and research has been done to reduce thisresource burden, thus reducing computational resources and power consumption.One of the most interesting topics in this area is the idea of encoding network weightswith progressively lesser number of bits. Since these weights are typically real numbers,some experiments managed to reduce number of bit from64(double precision) to32(singleprecision) and even to16(half precision), with no drawback on network accuracy.Probably, the most interesting proposed idea as an alternative to float numbers is the Posit,presented by John L. Gustafson as an extension of one of his works about overcoming float number.One of the greatest advantages of this new type is the simplicity of needed circuitry forits implementation, thus obtaining a faster and smaller architecture with regards to the float one.