Balancing storage, efficient computing, accuracy, mapping to silicon and power consumption is a challenge when trying to use low bit-depth neural network. Case studies encompassing anomaly detector and classifier model design are complex tasks if neural networks are investigated targeting ultra-low power devices such as sensors and microcontrollers. Deeply Quantized Neural Networks (DQNNs) offer the most interesting approach to these tasks. The design and the training of DQNN also is not a trivial task. Unfortunately, current off the shelf microcontrollers are not yet able to exploit their potentialities. Realization of custom energy efficient hardware accelerators sometime may represent a viable alternative in terms of energy efficiency, especially applied to a raising field such as in-sensing neural computing. Hybrid Neural Networks variants developed with experimental deep learning tools, can achieve interesting accuracies compared to more traditional design approaches. In this talk all those aspects will be discussed with reference to latest efforts of ST including a) tools for efficient deployment on micro controllers for image processing b) custom ultra-low power hardware circuitry for real-time execution of the Hybrid Neural Network with traditional CMOS technologies and implemented with field-programmable gate array, c) latest ST solutions for in sensor deep learning computing. Part of the talk will include associated demo and code inspection.
Danilo Pau graduated in Electronic Engineering at Politecnico di Milano in 1992. Since 1991, Danilo joined STMicroelectronics. He worked on HDMAC and MPEG2 video memory reduction, video coding, transcoding, embedded graphics, and computer vision. Currently, his work focuses on developing tools for deploy deep learning on tiny devices with associated applications.
Since 2019 Danilo is an IEEE Fellow. He served as Industry Ambassador coordinator for IEEE Region 8 South Europe, was ice-chairman of the “Intelligent Cyber-Physical Systems” Task Force within IEEE CIS, and Member of the Machine Learning, Deep Learning and AI in the CE (MDA) Technical Stream Committee IEEE Consumer Electronics Society (CESoc).
With over 80 patents, 124 publications, 113 MPEG authored documents and 50 invited talks/seminars at various worldwide Universities and Conferences, Danilo's favorite activity remains mentoring undergraduate students, MSc engineers and PhD students from various universities in Italy, US, France, and India.