Analog and digital circuits for machine learning: Page 6 of 6

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.

Concluding Remarks

To summarize, it is obvious that the vibrant nature of the machine learning field will bring about new and interesting technologies that will mature throughout generations to address various market needs. It appears that analog solutions open up a huge potential in a subset of the overall field of neural-network compute engines. Once they are more established they may very well become a complementary element in a variety of neural compute solutions and may address some challenging use cases. Nonetheless, it is hard to foresee analog-based solutions become dominant in this field in light of their limited scalability, technology node sensitivity, and the fact that the solution they offer is relevant to a relatively limited subset of applications while digital solutions offer a valid alternative that is flexible, relatively easy to implement and good enough to meet many product needs.

Avi Baum, is co-founder and chief technology officer at Hailo Technologies Ltd. and a former CTO at Texas Instruments' wireless technology group. Hailo, founded in 2017, is  developing a processor architecture to accelerate neural network processing on edge devices that could be installed in autonomous vehicles, drones and smart home appliances such as personal assistants, smart cameras and smart TVs.

Related links and articles:

News articles:

Israel startup funded to develop deep learning processor

Analog NN startup signs up Infineon microphones

ARM launches two machine learning processors

Intel to launch "commercial" Nervana NN processor in 2019

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