Juhyun Lee
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Efficient Heterogeneous Video Segmentation at the Edge
Jamie Lin
Siargey Pisarchyk
David Cong Tian
Sixth Workshop on Computer Vision for AR/VR (CV4ARVR) (2022)
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We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across multiple dimensions of specifications for the neural architectures and operations on top of already light-weight backbones, targeting commercially available edge inference engines. We further analyze and optimize the heterogeneous data flows in our systems across the CPU, the GPU and the NPU. Our approach has empirically factored well into our real-time AR system, enabling remarkably higher accuracy with quadrupled effective resolutions, yet at much shorter end-to-end latency, much higher frame rate, and even lower power consumption on edge platforms.
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On-Device Neural Net Inference with Mobile GPUs
Nikolay Chirkov
Yury Pisarchyk
Mogan Shieh
Fabio Riccardi
Efficient Deep Learning for Computer Vision CVPR 2019 (ECV2019) (to appear)
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On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy. Running such a compute-intensive task solely on the mobile CPU, however, can be difficult due to limited computing power, thermal constraints, and energy consumption. App developers and researchers have begun exploiting hardware accelerators to overcome these challenges. Recently, device manufacturers are adding neural processing units into high-end phones for on-device inference, but these account for only a small fraction of hand-held devices. In this paper, we present how we leverage the mobile GPU, a ubiquitous hardware accelerator on virtually every phone, to run inference of deep neural networks in real-time for both Android and iOS devices. By describing our architecture, we also discuss how to design networks that are mobile GPU-friendly. Our state-of-the-art mobile GPU inference engine is integrated into the open-source project TensorFlow Lite and publicly available at https://tensorflow.org/lite.
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MediaPipe: A Framework for Perceiving and Processing Reality
Camillo Lugaresi
Jiuqiang Tang
Hadon Nash
Chris McClanahan
Esha Uboweja
Michael Hays
Fan Zhang
Chuo-Ling Chang
Ming Yong
Wan-Teh Chang
Wei Hua
Manfred Georg
Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019
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Building an application that processes perceptual inputs involves more than running an ML model. Developers have to harness the capabilities of a wide range of devices; balance resource usage and quality of results; run multiple operations in parallel and with pipelining; and ensure that time-series data is properly synchronized. The MediaPipe framework addresses these challenges. A developer can use MediaPipe to easily and rapidly combine existing and new perception components into prototypes and advance them to polished cross-platform applications. The developer can configure an application built with MediaPipe to manage resources efficiently (both CPU and GPU) for low latency performance, to handle synchronization of time-series data such as audio and video frames and to measure performance and resource consumption. We show that these features enable a developer to focus on the algorithm or model development, and use MediaPipe as an environment for iteratively improving their application, with results reproducible across different devices and platforms. MediaPipe will be open-sourced at https://github.com/google/mediapipe.
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