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Low-power image recognition challenge

机译:低功耗图像识别挑战

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摘要

Significant progress has been made in recent years using computer programs recognizing objects in images. Meanwhile, many cameras are embedded in battery-powered systems (such as mobile phones, wearable devices, and drones) and energy efficiency is essential. Even though many research papers have been published on the topics related to low power and image recognition, there does not exist a common metric for comparing different solutions in terms of (1) energy efficiency and (2) accuracy in recognition. Low-Power Image Recognition Challenge (LPIRC) is, to our knowledge, the only on-site competition that considers both energy consumption and recognition accuracy. LPIRC was held as one-day workshops in the Design Automation Conference in 2015 and 2016. Each participating team brought their own system to the workshops. The referee system of LPIRC includes (1) an intranet, (2) a power meter, and (3) an HTTP server that provided the images and accepted the answers from the contestants' systems. The scores were the ratio of recognition accuracy and the energy consumption. The winner of 2016 was able to analyze 7,347 images and achieve 9.44% normalized mAP (mean average precision) with average power consumption of 4.7 W. Another team analyzed 1,020 images and achieved 25.7% normalized mAP.
机译:近年来,使用计算机程序识别图像中的物体已经取得了重大进展。同时,许多相机都嵌入了电池供电的系统(例如手机,可穿戴设备和无人机)中,能源效率至关重要。尽管已经发表了许多有关低功耗和图像识别的主题的研究论文,但在(1)能源效率和(2)识别精度方面,尚不存在用于比较不同解决方案的通用指标。据我们所知,低功耗图像识别挑战赛(LPIRC)是唯一同时考虑能耗和识别准确性的现场竞赛。 LPIRC在2015年和2016年的设计自动化会议中作为为期一天的讲习班举行。每个参加团队都将自己的系统带入了讲习班。 LPIRC的裁判系统包括(1)内联网,(2)功率计和(3)HTTP服务器,该HTTP服务器提供图像并接受参赛者系统的回答。分数是识别准确率与能耗之间的比率。 2016年的获胜者能够分析7,347张图像并获得9.44%的标准化mAP(平均平均精度),平均功耗为4.7W。另一个团队分析了1,020张图像并获得了25.7%的归一化mAP。

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