We held our forth tinyML Talks webcast with two presentations:
Hans Reyserhove from Facebook has presented Embedded Computer Vision Hardware through the Eyes of AR/VR and Jamie Campbell from Synopsys has presented Using TensorFlow Lite for Microcontrollers for High-Efficiency NN Inference on Ultra-Low Power Processor on May 14, 2020 at 8:00 AM and 08:30 AM Pacific Time.
Hans Reyserhove (left) and Jamie Campbell (right)
“Embedded Computer Vision Hardware through the Eyes of AR/VR”
Postdoctoral Research Scientist, Facebook Reality Labs
Augmented reality is an emerging technology that requires pushing the curve on almost all relevant fronts: computer vision algorithms and ML pipelines, sensing and processing hardware, memories, power consumption and system form factor. This talk will dig deeper on the technological challenges that are being solved today to make augmented reality happen. Although there are parallels with other embedded CV systems, a few key differentiators are essential to AR. Essential is the technology stack to make it all happen: image sensors, interfaces and processing hardware are a few blocks under consideration that ultimately guide the system-level trade-offs. These trade-offs are further illustrated by applying them to the key always-on computer vision and ML pipelines necessary for augmented reality. A lot of these considerations translate to the bigger tinyML and embedded computer vision design space.
Hans Reyserhove is a Postdoctoral Research Scientist at Facebook Reality Labs. His research focuses on intelligent vision systems and sensing technologies for Augmented & Virtual Reality. He holds a PhD from University of Leuven, Belgium, focused on design of energy-efficient microcontroller systems and better-than-worst-case silicon systems. He has a M.S. degree focused on CMOS image sensors with pixel-level A/D conversion for extreme parallelism. His main interests lie in design, prototyping & optimization of silicon systems, including image sensors, hardware accelerators and computer vision applications.
"Using TensorFlow Lite for Microcontrollers for High-Efficiency NN Inference on Ultra-Low Power Processors"
Software Engineering Manager, Synopsys
Deeply-embedded AIoT applications doing neural network (NN) inference need to achieve specified real-time performance requirements on systems with limited memory and power budget. Meanwhile, developers want a convenient way of migrating their NN graph designs to an embedded environment. In this talk, we will describe how specific hardware extensions on embedded processors can vastly improve the performance of NN inference operations, which allows targets to be met while consuming less power. We will then show how optimized NN inference libraries can be integrated with well-known ML front-ends to facilitate development flows.
To illustrate these concepts, we’ll show the Synopsys MLI Machine Learning Inference library running on a DSP-enhanced DesignWare® ARC® EM processor and explain how it was integrated with TensorFlow Lite for Microcontrollers (TFLM). To conclude, we will showcase Himax Technologies’ WE-I Plus silicon, a very low-power SoC targeted at AIoT applications that supports both MLI and TFLM.
Jamie Campbell is a Software Engineering Manager at Synopsys, leading teams responsible for the development of the Machine Learning Inference (MLI) library for Synopsys ARC processors and the creation of compelling demos and reference applications for the Synopsys Embedded Vision processors. Prior to focusing on machine learning, Jamie has worked in various capacities as an embedded software specialist, including R&D engineer, Field Applications Engineer and Corporate Applications Engineer at Precise Software Technologies, ARC International, Virage Logic and now Synopsys. Jamie holds a Bachelor of Science in Electrical Engineering from the University of Calgary, Canada.
Feel free to ask your questions on this thread and keep the conversation going!