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Starfish: Resilient Image Compression for Aiot Cameras

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Starfish: Resilient Image Compression for Aiot Cameras Starfish: Resilient Image Compression for AIoT Cameras Pan Hu Junha Im [email protected] [email protected] Stanford University Stanford University, Samsung Electronics Zain Asgar Sachin Katti [email protected] [email protected] Stanford University Stanford University IoT Camera Lossy Image Lossy IoT Gateway ABSTRACT Compression Link Cameras are key enablers for a wide range of IoT use cases including smart cities, intelligent transportation, AI-enabled farms, and more. These IoT applications require cloud software (including models) to Starfish act on the images. However, traditional task oblivious compression techniques are a poor fit for delivering images over low power IoT networks that are lossy and limited in capacity. The key challenge is their brittleness against packet loss; they are highly sensitive to small amounts of packet loss requiring retransmission for transport, which further reduces the available capacity of the network. We JPEG propose Starfish, a design that achieves better compression ratios and is graceful with packet loss. In addition to that, Starfish features content-awareness and task-awareness, meaning that we can build Figure 1: Illustration of uploading image from IoT camera specialized codecs for each application scenario and optimized to Gateway with Starfish and conventional JPEG-based for task objectives, including objective/perceptual quality as well methods. DNN-based Starfish is resilient to packet loss as AI tasks directly. We carefully design the DNN architecture and results in better image quality. and use an AutoML method to search for TinyML models that work on extremely low power/cost AIoT accelerators. Starfish is on Neural Gaze Detection, June 03–05, 2018, Woodstock, NY. ACM, New York, not only the first image compress framework that works ona$3 NY, USA, 14 pages. https://doi.org/10.1145/1122445.1122456 AIoT accelerators but also outperforms JPEG, a well-established baseline, by up to 3×, in terms of bandwidth efficiency and up to 2.5× as efficient in energy consumption. It also features graceful 1 INTRODUCTION and gradual performance degradation in the presence of packet Recent advances in computer vision technologies have been a key loss. The application-level simulation indicates that Starfish could enabler for the pervasive growth of vision-based IoT applications deliver 3.7× images while providing better image quality. ranging from smart cities, intelligent transportation, AI-enabled factory to farms [51]. Cities and construction sites use cameras for CCS CONCEPTS intelligent transportation and monitoring to increase safety and security [2, 10, 44]. Oil refineries and farms use cameras to perform • Computer systems organization ! Neural networks; • Net- predictive maintenance and irrigation [49, 66]. The camera, being works ! Error detection and error correction; Cyber-physical one of the most information-rich and versatile sensors maximizes networks. it’s potential when coupled with powerful machine perception KEYWORDS algorithms. Although some computer vision applications can be performed artificial intelligence, internet of things, compression, resilient onsite without cloud support, many applications need to leverage ACM Reference Format: the vast compute and storage available on the cloud. These applica- Pan Hu, Junha Im, Zain Asgar, and Sachin Katti. 2018. Starfish: Resilient tions typically require heavy computation, perform computation Image Compression for AIoT Cameras. In Woodstock ’18: ACM Symposium on data from multiple camera streams, or store the raw data for archival purposes and future applications. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed Transferring images efficiently to the cloud is essential for for profit or commercial advantage and that copies bear this notice and the full citation many battery-powered IoT cameras. Current solutions rely on LP- on the first page. Copyrights for components of this work owned by others than ACM WAN(Low Power Wide Area Network) such as LoRaWAN and 5G must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a IoT for communication that tends to have very limited bandwidth fee. Request permissions from [email protected]. and high packet loss rate due to low transmit power, high path loss, Woodstock ’18, June 03–05, 2018, Woodstock, NY and collision. Increasing the reliability of the network is possible. © 2018 Association for Computing Machinery. ACM ISBN 978-1-4503-XXXX-X/18/06...$15.00 However, this comes at the cost of more complexity and retrans- https://doi.org/10.1145/1122445.1122456 mission of data, significantly reducing the total network capacity. Woodstock ’18, June 03–05, 2018, Woodstock, NY Pan Hu, Junha Im, Zain Asgar, and Sachin Katti Studies show that reducing the packet error rate by 20% reduces according to the learning performance predictor. We start with network capacity by 5.2 times [75] in LoRaWAN. sampling a small fraction of the design space and train them until The highly limited network capacity in LPWAN call for extreme they converge. The training curves are used to train the learning image compression. However, traditional image compression such performance predictor based on LSTM that predicts performance as JPEG is a poor fit for most IoT applications; specifically, wemake from first a few epochs of the training curve. We then train each the following observations about the design objectives of JPEG: configuration in the design space for a few epochs and continue to 1) designed to be used on reliable networks; 2) general-purpose train only if the predicted performance falls in the top percentile. compression applicable to a variety of images; 3) content and task We showcase our design and test a tiny DNN that runs on a $3 AIoT oblivious. While JPEG is an excellent general-purpose image com- accelerator, as well as the more powerful Google Edge TPU. pression algorithm, it’s not necessarily the best fit for IoT appli- Benchmark results on four large public datasets indicate that we cations where the task objective and context are typically clear. can achieve the same task objective while using only a small fraction Further, since many IoT devices use low-power networks with un- of the energy/bandwidth, thus significantly reducing the cost of reliable transport making JPEGs brittleness to any packet loss a operation and accommodating more AIoT cameras and increase substantial limitation. task performance. We summarize our contributions as follows: If our goal is to send a lossy-compressed image over LPWAN, • We propose Starfish, a lossy image compression framework why do we need to assume lossless transmission of compressed designed for LPWAN that processes all the information loss data? We present Starfish, an application layer solution that in the application layer, thus simplifying wireless protocol de- works with intrinsically lossy wireless links. Starfish avoids inef- sign, improves the network throughput and battery life of IoT ficient retransmission by addressing information loss and data loss cameras. altogether in the application space utilizing information about the • To our best knowledge, Starfish is the first DNN-based com- context and task objectives. Inspired by recent advances in DNN pression framework that runs efficiently on low-cost AIoT de- hardware and software, Starfish uses a tiny DNN to generate vices. We generate DNN configuration with NAS automatically, a loss-resilient, unstructured compressed representation that de- making it future-proof and generalizable to a diverse set of DNN grades gracefully in the presence of data loss, as shown in Figure 1. architectures and AIoT hardware. Unlike structured representation in JPEG, the information is dis- • Benchmarks on a large-scale image dataset and IoT links sug- tributed uniformly in DNN representation: reconstructing an image gest that Starfish is up to 3∼4× as efficient in compression takes inputs from all bytes in representation rather than relying on size, and up to 2.5× as efficient in terms of time and energy effi- specific bytes. DNN does not need the header in which dataloss ciency for lossless traffic, due to the task-awareness and content- could be fatal. As a result, image quality degrades gracefully as data awareness of Starfish. Simulation of 100 to 1000 nodes sug- loss occurs rather than completely damage part of the image. gests Starfish could deliver more than 3.7× images with better The ability to tolerate packet loss in application space not only image quality in lossy traffic scenarios. simplifies MAC(Medium Access Control) layer protocol but also brings significant benefits in energy consumption and network ca- pacity. LPWAN nodes can send at higher bitrates without the need 2 BACKGROUND AND MOTIVATION to wait during the back-off period before retransmission. Such abil- We describe the background work motivating the design of our ity is extremely powerful in LPWAN, where thousands to millions streaming framework in this section, starting with an analysis of of nodes are connected to each base station or gateway, and nodes network/compression and AIoT hardware, then show the challenges need to conserve energy as much as possible to extend battery life. of using conventional image compression and our approach to deal Nevertheless, our DNN-based
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