CAREER: Efficient, Dynamic, Robust, and On-Device Continual Deep Learning with Non-Volatile Memory based In-Memory Computing System

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。在过去的几十年里，在为大数据处理开发高性能和节能的计算解决方案方面存在着巨大的挑战。同时，由于人工智能（AI），特别是深度神经网络（dnn）的蓬勃发展，这种大数据处理需要高效、智能、快速、动态、鲁棒和设备上自适应的认知计算。然而，由于硅基半导体器件中众所周知的功率墙、传统冯-诺伊曼计算架构中的内存墙以及计算/内存密集型DNN计算算法，现有的计算解决方案并不能充分满足这些要求。该项目旨在通过协作开发一种混合内存计算（IMC）硬件平台，整合新兴的非易失性电阻存储器（RRAM）和静态随机存取存储器（SRAM）技术的优势，并结合IMC感知的深度学习算法创新，在开发内存中人工智能计算系统方面取得系统突破。该项目的总体目标是设计、实现并实验验证一种新的混合内存计算系统，该系统在能源效率、推理准确性、时空动态、鲁棒性和设备上学习方面进行协同优化，这将极大地推进基于人工智能的大数据处理领域，如计算机视觉、自动驾驶、机器人等。该研究还将扩展到一个教育平台，提供一个用户友好的学习框架，并将服务于K-12学生、本科生、研究生和代表性不足的学生的教育目标。该项目将为内存中人工智能计算的新范式提供知识和科学原理和工具，并在能效、速度、动态、鲁棒性和设备上学习能力方面取得重大进步。这个跨层项目涵盖了从设备、电路和架构到深度神经网络算法的探索。首先，将设计、优化和制造一种基于RRAM-SRAM的混合内存计算芯片。其次，基于这种新的计算平台，将开发设备上的时空动态神经网络结构，以提供增强的运行时计算轮廓（延迟，资源分配，工作负载，功率预算等），并提高系统对硬件固有和对抗性噪声注入的鲁棒性。然后，利用开发的计算平台研究有效的设备上学习方法。最后，将开发端到端DNN训练、优化、映射和评估CAD工具，该工具集成了已开发的硬件平台和算法创新，用于优化软件和硬件协同设计，以实现用户定义的延迟、能效、动态、准确性、鲁棒性、设备上自适应等多目标。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Over past decades, there have existed grand challenges in developing high performance and energy-efficient computing solutions for big-data processing. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Networks (DNNs), such big-data processing requires efficient, intelligent, fast, dynamic, robust, and on-device adaptive cognitive computing. However, those requirements are not sufficiently satisfied by existing computing solutions due to the well-known power wall in silicon-based semiconductor devices, the memory wall in traditional Von-Neuman computing architectures, and computation-/memory-intensive DNN computing algorithms. This project aims to foster a systematic breakthrough in developing AI-in-Memory computing systems, through collaboratively developing ahybrid in-memory computing (IMC) hardware platform integrating the benefits of emerging non-volatile resistive memory (RRAM) and Static Random Access Memory (SRAM) technologies, as well as incorporating IMC-aware deep-learning algorithm innovations. The overarching goal of this project is to design, implement, and experimentally validate a new hybrid in-memory computing system that is collaboratively optimized for energy efficiency, inference accuracy, spatiotemporal dynamics, robustness, and on-device learning, which will greatly advance AI-based big-data processing fields such as computer vision, autonomous driving, robotics, etc. The research will also be extended into an educational platform, providing a user-friendly learning framework, and will serve the educational objectives for K-12 students, undergraduate, graduate, and under-represented students.This project will advance knowledge and produce scientific principles and tools for a new paradigm of AI-in-Memory computing featuring significant improvements in energy efficiency, speed, dynamics, robustness, and on-device learning capability. This cross-layer project spans from device, circuit, and architecture to DNN algorithm exploration. First, a hybrid RRAM-SRAM based in-memory computing chip will be designed, optimized, and fabricated. Second, based on this new computing platform, the on-device spatiotemporal dynamic neural network structure will be developed to provide an enhanced run-time computing profile (latency, resource allocation, working load, power budget, etc.), as well as improve the robustness of the system against hardware intrinsic and adversarial noise injection. Then, efficient on-device learning methodologies with the developed computing platform will be investigated. In the last thrust, an end-to-end DNN training, optimization, mapping, and evaluation CAD tool will be developed that integrates the developed hardware platform and algorithm innovations, for optimizing the software and hardware co-designs to achieve the user-defined multi-objectives in latency, energy efficiency, dynamics, accuracy, robustness, on-device adaption, etc.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.