We held our twelfth tinyML Talks webcast with two presentations: Venkat Rangan from tinyVision.ai has presented Low power CV meets the real world and Theocharis Theocharides from University of Cyprus has presented Towards Ultra-Low Power Embedded Object Detection on August 4, 2020 at 8:00 AM and 8:30 AM Pacific Time.
Venkat Rangan (left) and Theocharis Theocharides (right)
As the tinyML community is acutely aware, adding Vision capability to a battery powered IoT device is non-trivial. The tremendous amount of vision data that needs to be processed necessitates the use of HW accelerators as well as clever algorithms that take advantage of data locality, sparsity and so on. A real world CV enabled IoT device requires attention to a range of other practical issues ranging from indoor/outdoor location, orientation, optics, sensor selection etc. This talk touches upon some of the practical considerations, tradeoffs and issues inherent in the design of a tinyCV system.
Venkat Rangan is the founder of tinyVision.ai Inc., a product design and consulting company specializing in IoT devices incorporating Computer Vision. Prior to founding tinyVision, Venkat was a Director of Engineering at Qualcomm Research where he co-founded and led the R&D of the ultra-low power Glance CV solution. He is the holder of more than 60 granted patents in various fields including low power conventional and neuromorphic vision. Venkat holds a BSEE from the Indian Institute of Technology, Roorkee and an MSEE from the University of Cincinnati. He can be reached at firstname.lastname@example.org. www.tinyvision.ai
Embedded computer vision is nowadays adopted in several computing devices, consumer electronics and cyber-physical systems. Visual edge intelligence is a growing necessity for emerging applications where real-time decision is vital. Object detection, the first step in such applications, achieved tremendous improvements in terms of accuracy due to the emergence of Convolutional Neural Networks (CNNs) and Deep Learning. However, such complex paradigms require extensive resources, which prevents their deployment on resource-constraint mobile and embedded devices that simultaneously need to process high resolution images. Common approaches in reducing resources involve techniques such as quantization, pruning, compression, etc. While these techniques are efficient up to a certain aspect, they are built on traditional computationally inspired approaches. On the other hand, mammalian vision utilizes saliency and memory among other techniques, and limits attention during a visual search within a significantly limited search space. In this talk therefore, I will present our efforts to reduce the processing demands of edge-based CNN inference, via inclusion of a hierarchical framework that enables to detect objects in high-resolution video frames, and maintain the accuracy of state-of-the-art CNN-based object detectors, validated on UAV platforms in various applications involving car and pedestrian detection.
Theocharis (Theo) Theocharides holds a Ph.D. in Computer Engineering from Penn State University, working in the areas of low-power, resource constrained computer architecture and embedded systems design with emphasis on computer vision and machine learning applications. Along with his students, his research encapsulates the design, development, implementation and deployment of low-power and reliable on-chip application-specific architectures, real-time embedded systems design, with emphasis on acceleration of computer vision and artificial intelligence algorithms in hardware, geared towards edge computing, and in utilizing reconfigurable hardware towards self-aware, evolvable and robust intelligent edge computing systems. Theo is a Senior Member of the IEEE, a member of the ACM, and currently he is an Associate Editor for IEEE Consumer Electronics magazine, ACM’s Journal on Emerging Technologies, the IET Computers and Digital Techniques, and the ETRI journal. He is also currently serving as the Application Track Chair for the Design, Automation and Test in Europe (DATE) Conference.
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