目前，欧洲研究团体正在开展一项新的研发项目，旨在为下一代汽车高级驾驶员辅助系统（ADAS）提供高性能、低功耗的嵌入式图像处理应用。

该项目代号是TULIPP (Towards UbiquitousLow-power Image Processing Platforms/面向无处不在的低功耗图像处理平台) 简单来说，该项目的目标是在2018年前，为设计师提供一个参考开发平台，协助他们进行基于视觉的系统设计。该平台将结合功效等多种软、硬件参数的定义规则，降低系统开发的时间和成本。

未来，该指南可用于低功耗可扩展面板的开发，从而满足嵌入式系统对尺寸、重量及功率（SWaP）、低功耗操作系统、图像处理库及能量感知工具链的典型需求。该项目的目标之一在于,协助软件设计师更加轻松地应对多核设备带来的并行编程问题，以及不同编程模型和应用程序界面（API）间存在的异构性。

此外，TULIPP项目还将开发三个演示用例，进行项目的概念验证和参考平台验证。据了解，演示用例将覆盖汽车、航空航天和医疗等多个领域出现的各种复杂图像处理需求，其中一个用例中的车辆ADAS“智能”嵌入式视觉系统除了可以进行低级别的图像处理外，还可以智能解读图像内容，从而为驾驶员提供更安全的驾驶体验。

 “图像处理技术的应用日益广泛，横跨多个行业，且复杂程度远超以往任何时候。”法国泰雷兹（Thales）集团高管、TULIPP项目协调员Philippe Millet表示，“TULIPP的参考平台将极大地促进系统整合、图像处理创新及空闲电源管理方面的发展，从而在如今基于视觉的系统越来越复杂的情况下，应对不断出现的新挑战。”

到2018年项目结束时，TULIPP项目预计可以将图像处理应用的峰值性能耗电比提高4倍，平均性能耗电比提高10倍。在项目正式结束之后，该平台预计可以继续提升图像处理应用的性能功耗比，并在2023年前达到200倍的水平。现阶段，TULIPP拿到了欧洲最大研究项目Horizon 2020接近400万欧元的经费。据了解，Horizon 2020项目将在2014到2020年间提供近800亿欧元经费，以推动有价值科学创新技术的市场化进程。

未来，TULIPP项目将与各标准组织紧密合作，从整个行业的角度出发，推广该参考平台积累的经验和总结的标准。

TULIPP联盟成员即有行业从业人士，也有专家学者。除了担任牵头人和协调员的法国泰雷兹外，项目参与方还包括法国Efficient Innovation SAS、德国Fraunhofer IOSB、比利时Hipperos、挪威科技大学、德国波鸿大学、Sundance Multiprocessor Technology和Synective Labs。

A new European research initiative to develop high-performance, energy-efficient embedded systems for image-processing applications has implications for next-generation automotive ADAS (advanced driver-assistance systems). 

The TULIPP (Towards Ubiquitous Low-power Image Processing Platforms) research consortium aims to develop by 2018 a reference platform for vision-based system designers. The platform will incorporate guidelines that define various hardware and software parameters including power efficiency, with the goal of reducing development time and cost. 

The guidelines will be used to develop a scalable low-power board designed to meet typical embedded-systems requirements of size, weight and power (SWaP), a low-power operating system and image processing libraries, and an energy-aware tool chain. One goal is to help software designers deal more easily with parallel programming issues presented by multicore devices, as well as the heterogeneity of different programming models and application program interfaces (APIs).

In addition, TULIPP will develop three use-case demonstrators as proof-of-concept and validation of the reference platform. The use cases will cover the emerging complex image processing requirements of various industry sectors including automotive, aerospace and medical. One use case involves a "smart" automotive embedded vision system for automotive ADAS that, in addition to the low-level image processing, will intelligently interpret what is on the images to deliver safer driving experiences.

“Image processing applications stretch across an increasingly broad range of industrial domains and are reaching a higher level of complexity than ever before,” said Philippe Millet of Thales and TULIPP project coordinator. “The TULIPP reference platform will give rise to significant advances in system integration, processing innovation and idle power management to cope with the challenges this presents in increasingly complex vision-based systems.”

When the project concludes in 2018, TULIPP expects its work to extend the peak performance-per-watt of image processing applications by 4x and average performance-per-watt by 10x. Beyond the official completion, it is expected that this will be extended to 100x and 200x by 2023. TULIPP is being funded with nearly €4 million from Horizon 2020, the EU's biggest research program. The organization has nearly €80 billion in funding available from 2014 to 2020 for bringing science and technology innovations to market.  

TULIPP plans to work closely with various standards organizations to propose the formal adoption, on an industry-wide basis, of new standards derived from its reference platform.

The TULIPP consortium members represent both industry and academia. Along with project lead and coordinator Thales, the members include Efficient Innovation SAS, Fraunhofer IOSB, Hipperos, Norges Teknisk-Naturvitenskapelige Universitet, Ruhr-Universität Bochum, Sundance Multiprocessor Technology, and Synective Labs.

Author: Jennifer Shuttleworth
Source: SAE Automotive Engineering Magazine