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

该项目建立了NSF大学习（CBL）产学研合作研究中心（I/UCRC），以加速深度学习在各种嵌入式应用中的创新和影响。愿景是创造智能，走向智能驱动的社会。通过催化来自教师、学生、行业合作伙伴和联邦机构的各种专业知识的融合，CBL寻求创造最先进的深度学习方法和技术，并实现智能应用，改变广泛的领域，如商业、医疗保健、物联网和网络安全。这一及时的举措创造了一个独特的平台，为我们的下一代人才提供具有社会相关性和意义的尖端技术。密苏里大学堪萨斯城分校（UMKC）网站专注于移动和物联网应用的嵌入式系统中的深度学习。它基于一个名为DeepLite的框架，用于深度学习模型压缩和加速，可以在计算、存储、通信和功率能力非常有限的嵌入式系统中适应尖端的深度学习功能。DeepLite允许嵌入式深度学习模型训练和压缩功率，存储，计算复杂性权衡与学习性能的目标嵌入式应用，如沉浸式内容捕获，深度和动作传感，视觉监控，下一代图像和视频压缩和通信。CBL预计将对机器学习算法、系统和应用研究产生广泛而持久的影响，加速深度学习技术在现实世界中的创新和采用，在教育、医学、媒体、安全和国防等社会各个方面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、技术商业孵化和社区参与。它促进了学术界和工业界之间更紧密的互动和思想的交流，拓宽了教师和学生的研究视野，同时有助于缩短新技术的影响时间和上市时间。中心存储库将托管在http://nsfcbl.org上。数据、代码和文件将在CBL服务器上得到很好的组织和维护，在中心期间超过五年或更长时间。内部代码库将由GitLab管理。在软件包经过良好的文档和测试后，它们将由流行的公共代码托管服务（如GitHub和Bitbucket）发布和管理。

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.