Phase I IUCRC University of Missouri-Kansas City: Center for Big Learning (CBL)

该项目建立了NSF行业/大学大学习协作研究中心(I/UCRC)，以加速深度学习在各种嵌入式应用中的创新和影响。我们的愿景是创造智能，走向智能驱动的社会。通过促进来自教职员工、学生、行业合作伙伴和联邦机构的不同专业知识的融合，CBL寻求创建最先进的深度学习方法和技术，并实现智能应用，从而改变商业、医疗保健、物联网和网络安全等广泛领域。这一及时的倡议为我们的下一代人才创造了一个独特的平台，使其能够利用具有社会意义和重要意义的尖端技术。密苏里大学堪萨斯城分校(UMKC)的网站专注于针对移动和物联网应用的嵌入式系统的深度学习。它基于一个名为DeepLite的深度学习模型压缩和加速框架，可以适应计算、存储、通信和电力能力非常有限的嵌入式系统中的尖端深度学习能力。DeepLite允许对嵌入式深度学习模型进行培训和压缩，以实现电力、存储、计算复杂性与目标嵌入式应用程序的学习性能之间的权衡，例如身临其境的内容捕获、深度和动作感知、视觉监控、下一代图像和视频压缩以及通信。CBL有望对机器学习算法、系统和应用研究产生广泛和持久的影响，加速深度学习技术在现实世界中的创新和采用，使从教育、医疗、媒体到安全和国防的社会方方面面都能实现变革性的新能力和新应用。CBL无缝集成了创新、工程教育、技术企业孵化和社区参与。它促进了学术界和产业界之间更紧密的互动和思想的交叉授粉，拓宽了院系和学生的研究视野，同时有助于缩短新技术产生影响和上市的时间。中心存储库将托管在http://nsfcbl.org.在中心五年以上的时间里，数据、代码、文件将在CBL服务器上得到很好的组织和维护。内部代码库将由GitLab管理。在软件包被很好地记录和测试之后，它们将由流行的公共代码托管服务发布和管理，如GitHub和BitBucket。

This project establishes the NSF Industry/University Collaborative Research Center (I/UCRC) for Big Learning (CBL) to accelerate innovation and impact of Deep Learning in various embedded applications. The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance. The University of Missouri at Kansas City (UMKC) site focuses on the deep learning in embedded systems for mobile and IoT applications. It is based on a framework called DeepLite for deep learning model compression and acceleration that can fit cutting edge deep learning capabilities in embedded systems with very limited computing, storage, communication and power capabilities. DeepLite allows embedded deep learning model training and compression for power, storage, computation complexity tradeoffs with learning performances for targeted embedded applications like immersive content capture, depth and action sensing, visual surveillance, next gen image and video compression and communication. CBL is expected to make wide ranging and long lasting impact to machine learning algorithm, system and application research, accelerating deep learning technology innovation and adoption in the real world, enable transformative new capabilities and new applications in all aspect of society, from education, medicine, media, to security and defense. CBL seamlessly integrates innovation, engineering education, technology business incubation, and community engagement. It facilitates closer interactions and cross pollination of ideas between academia and industry, broaden the research horizon for faculties and students, while help shrink the time to impact and time to market of new technology. The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket.