Neurally-Inspired Integration of Communication and Cognitive Computation in Hyperspace

超空间中通信和认知计算的神经启发集成

• 批准号: 2319198
• 负责人: Mohsen Imani
• 金额: $ 36万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319198&HistoricalAwards=false
• 关键词: Neurally; Inspired; Integration; Communication; Cognitive

The next generation of communication systems holds the potential to enhance every aspect of our lives. From enabling seamless global connectivity and telemedicine advancements to transforming transportation, fostering education and entrepreneurship, supporting disaster response, and driving economic growth. However, current computer systems face a challenge where communication and learning applications operate independently, leading to inefficiencies and delays caused by the need to coordinate between these separate layers. Merely improving battery life in mobile devices is insufficient to keep up with the growing demand for efficient processing, especially considering the rapid advancements in machine learning. This project seeks to address these fundamental issues by pioneering advanced technology for communication that seamlessly integrates channel coding with machine learning. By bringing these two domains together, we aim to achieve hyper-reliable communication and efficient machine learning processing. Moreover, we aim to develop future learning systems that can operate flawlessly even in noisy and unreliable network environments. This approach has the potential to provide users with the benefits of neurally-inspired learning models across various network, thereby significantly enhancing the efficiency and robustness of these systems. In addition to its technical advancements, this project recognizes the importance of education, diversity, and broader societal benefits. This project aims to establish a new exploratory undergraduate research program, fostering early research scholars who will contribute to the field of intelligent networks and computing systems. Furthermore, international collaboration will be a key component, allowing us to engage with global experts and share knowledge to advance the field as a whole.This research project presents a neural-inspired network system for robust and efficient data communication and information processing. It achieves this by redesigning communication and machine learning using a unified neural mathematical foundation with high compatibility. The main contributions of our proposal are as follows: (1) Designing rigorous communication schemes that work with the high-dimensional representation of transferred data in a mathematically tractable manner. This communication scheme consists of encoding/decoding methods that leverage redundant and holographic neural representations to achieve ultra-efficient and robust data communication. It also provides concrete network-level simulation and optimization to ensure the desired latency and responsiveness. (2) Fundamentally merging channel coding and learning by directly computing over transmitted data, eliminating the need for costly iterative data decoding. This eliminates the inter-layer overhead between communication and computation procedures that exists in prior communication schemes. (3) Designing a hardware platform that offers high parallelism and efficiency for both communication and computation. The proposed highly optimized hardware will automatically map applications to FPGA, equipped with a transceiver module, and leverage the parallelism provided by these platforms. Our framework will be evaluated using both complete simulation environments and real-world deployment in communication and application infrastructures. The prototype will be fully released under an established open-source library for public dissemination.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

下一代通信系统有潜力改善我们生活的各个方面。从实现无缝的全球连接和远程医疗进步到改变交通、促进教育和创业、支持救灾以及推动经济增长。然而，当前的计算机系统面临着通信和学习应用程序独立运行的挑战，导致由于需要在这些单独的层之间进行协调而导致效率低下和延迟。仅仅提高移动设备的电池寿命不足以满足对高效处理不断增长的需求，特别是考虑到机器学习的快速发展。该项目旨在通过开创性的先进通信技术来解决这些基本问题，该技术将信道编码与机器学习无缝集成。通过将这两个领域结合在一起，我们的目标是实现超可靠的通信和高效的机器学习处理。此外，我们的目标是开发未来的学习系统，即使在嘈杂和不可靠的网络环境中也能完美运行。这种方法有可能为用户提供跨各种网络的神经启发学习模型的好处，从而显着提高这些系统的效率和鲁棒性。除了技术进步之外，该项目还认识到教育、多样性和更广泛的社会效益的重要性。该项目旨在建立一个新的探索性本科研究项目，培养对智能网络和计算系统领域做出贡献的早期研究学者。此外，国际合作将是一个关键组成部分，使我们能够与全球专家合作并分享知识，以推动整个领域的发展。该研究项目提出了一种神经网络系统，用于稳健、高效的数据通信和信息处理。它通过使用具有高兼容性的统一神经数学基础重新设计通信和机器学习来实现这一目标。我们提案的主要贡献如下：（1）设计严格的通信方案，以数学上易于处理的方式处理传输数据的高维表示。该通信方案由编码/解码方法组成，利用冗余和全息神经表示来实现超高效和鲁棒的数据通信。它还提供具体的网络级模拟和优化，以确保所需的延迟和响应能力。 (2)通过直接计算传输的数据，从根本上融合信道编码和学习，消除了昂贵的迭代数据解码的需要。这消除了现有通信方案中存在的通信和计算过程之间的层间开销。 (3)设计一个具有高并行性和高效率的通信和计算硬件平台。所提出的高度优化的硬件将自动将应用映射到配备收发器模块的 FPGA，并利用这些平台提供的并行性。我们的框架将使用完整的模拟环境以及通信和应用程序基础设施中的实际部署进行评估。该原型将在已建立的开源库下全面发布，以供公众传播。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。