SHF: Small: Efficient and Accurate Learning with Low-Precision Components: A Cortex-Inspired Approach

以小尺寸实现高性能和高能效是计算机工程的核心挑战。该项目的目标是开发具有尖端纳米器件的技术，以实现自学习芯片。它将与前端传感器集成，实时处理信息，并消耗超低能耗。这一成功可能会对社会产生影响，为多种新兴应用、移动的视觉和自动驾驶汽车等带来广泛的好处。该项目的跨学科性质，以及与工业界的频繁互动，将为教育和培训最先进的科学和技术提供一个理想的平台。它将通过新的课程开发来提高智能系统设计的知识基础，让本科生和少数民族学生参与研究和实践，并参与为K-12学生定制的外展计划。此外，该项目将倡导基于网络的界面和研讨会，以传播最新的研究成果。微处理器已经成为我们现代生活中无处不在和至关重要的一部分。然而，他们在人工智能系统中面临着严重的问题，这需要大量的能量和数据来训练和操作复杂的算法。相反，各种大小的动物大脑在学习和准确性方面取得了显着的成就，其能量消耗远远低于人类工程系统。因此，该项目的中心主题是将大脑结构和功能的最新知识转移到神经形态设计中，为工程系统的改进产生新的见解，并在纳米级组件的严格精度限制下实现高精度和高能效。这些神经生物学原理包括具有低精度突触的近似学习规则、兴奋和抑制的神经基序以及分层网络模型。其目标是用更少的数据量和资源完成复杂的计算，并承诺比今天的微处理器在能源效率和性能方面有很大的改进。

Achieving high performance and high-energy efficiency with a small footprint is a central challenge of computer engineering. This project targets to develop technologies with cutting-edge nanoscale devices towards a self-learning chip. It will be integrated with front-end sensors, process the information in real-time, and consume ultra-low energy. The success is likely to have an impact on the society, bringing broad benefits to multiple emerging applications, mobile vision and autonomous vehicles to name a few. The interdisciplinary nature of this project, as well as the frequent interaction with industry, will provide an ideal platform for education and training of state-of-the-art science and technology. It will improve the knowledge base of intelligent system design through new curriculum development, engaging undergraduate and minority students in research and practice, and participating in outreach programs that are customized for K-12 students. Furthermore, this project will advocate the web-based interface and workshops to disseminate the latest research outcome. Microprocessors have been a ubiquitous and vitally important part in our modern-day life. However, they are facing severe issues in artificial intelligent systems, which require tremendous amount of energy and data to train and operate the sophisticated algorithm. On the contrary, animal brains at various sizes achieve remarkable feats of learning and accuracy at energy costs much lower than human-engineered systems. Therefore, the central theme of this project is to transfer the latest knowledge of the structure and function of brains into neuromorphic design, generate novel insights for improvement of the engineered system, and achieve high accuracy and high energy efficiency despite the severe precision constraints of the nanoscale components. These neurobiological principles include approximate learning rules with low-precision synapses, neural motifs of excitation and inhibition, and hierarchical network models. The goal is to accomplish complex computation with much less data volume and resources, and promise magnitudes of improvement in energy efficiency and performance than microprocessors today.