Analog and digital circuits for machine learning

July 24, 2018 // By Avi Baum
Avi Baum, chief technology officer of Hailo (Tel Aviv, Israel), compares the underlying principles and energy considerations behind analog and digital approaches to neural network implementation and machine learning circuits.


Since ancient days, nature has been the inspiration for mankind. Humanity has always looked to build systems that will mimic the elegant and efficient mechanisms nature has created to resolve challenges presented by the world we live in. Applications of this are observed in a wide variety of domains spanning from pharmacology to transportation.

Avi Baum, CTO of Hailo

In recent years, we are witnessing a renaissance in the field of ‘deep learning’, a domain that is attempting, ultimately, to enable the level of reasoning and intelligence that resembles human behavior. Identified as the organ that is considered the source of wisdom and intelligence, the ‘brain’ is the natural subject being explored in the journey to get closer to this target.

Distilled mathematical formulation in the form of artificial neural networks (ANN) are developed vis-a-vis the development of physical devices that will be able to run those networks effectively. Despite the fact that comparing computers and the human brain is quite mundane, their underlying structures are quite distinct. One easily spotted property of neural networks is their cellular nature. Thus, the structure of the basic ‘cell’ is among the aspects that are thoroughly explored, for the obvious reason that it gets repeated many times. Hence the importance of its efficiency. That will be the focus of this short article.

Next: A taste of theory


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