FuSe: Co-designing Continual-Learning Edge Architectures with Hetero-Integrated Silicon-CMOS and Electrochemical Random-Access Memory

机器学习和人工智能带来的变革性变化伴随着巨大的财务和环境成本。 这已经激发了更专门的计算机硬件和计算范例，诸如其中数据处理和数据存储由同一设备执行的存储器内计算，以更有效地执行这种数据密集型计算，特别是对于空间和能量供应通常有限的移动的设备和机器人。 然而，尽管该领域最近取得了进展，但实现能够连续学习其环境并相应地调整其行为而无需连接到集中式服务器的自主移动的设备仍然是一个艰巨的挑战。 该项目将通过材料科学家，设备工程师，电路设计师和计算机科学家的跨学科研究组合，共同设计核心存储器和信息处理设备，计算机架构和学习算法来解决这一限制。 该项目的成功完成将是迈向变革性计算系统的重要一步，使机器人和其他移动的设备能够以前所未有的能量和芯片面积效率自行执行学习。 通过研究型大学、少数民族服务机构和两年制社区学院的紧密合作，将研究与教育相结合，培养半导体研究与开发人才。在该项目中，将采用整体协同设计方法，实现由硅电路和先进的模拟电化学随机存取存储器（ECRAM）硬件异质集成的混合平台。 它作为一个强大的，紧凑的，节能的，具有成本效益的内存计算架构，使边缘学习能力的机器人在复杂和不断变化的环境中导航。 为了实现这一目标，该团队将开发高速ECRAM器件和阵列，其中包含新型纳米结构固态质子电解质。 将建立基于物理和实验验证的器件模型，以将材料特性与ECRAM性能相关联，作为工艺设计套件的一部分。 然后，ECRAM将与硅外围电路集成，以形成交叉杆微架构，这可以通过采用数字格式和精确缩放技术来减轻ECRAM器件非理想性的影响。 同时，自定义的同时定位和映射算法将与独特的硬件属性考虑共同设计。 最后，该团队将构建一个由ECRAM边缘学习加速器和集成在电路板上的辅助硅芯片组成的混合系统，用于机器人自主导航。 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。

The transformative changes brought by machine learning and artificial intelligence are accompanied by immense financial and environmental costs. This has inspired more specialized computer hardware and computing paradigms, such as in-memory-computing where the data processing and data storage are carried out by the same device, to perform such data-intensive calculations more efficiently, especially for mobile devices and robots where the space and energy supply are typically limited. However, despite recent advances in the field, it is still a daunting challenge to realize autonomous mobile devices capable of continuously learning their environment and adjusting their behaviors accordingly without connection to centralized servers. This project will address this limitation by co-designing the core memory and information-processing devices, the computer architecture, and the learning algorithms by an interdisciplinary combination of research by material scientists, device engineers, circuit designers, and computer scientists. Successful completion of the project will be a significant step toward a transformative computing system enabling robots and other mobile devices to perform learning by themselves with unprecedented energy and chip-area efficiencies. This project will also integrate research with education to grow the semiconductor research and development talent pool, through close collaborations among the research university, minority-serving institution, and two-year community college.In this project, a holistic co-design approach will be adopted to realize a hybrid platform, composed of silicon circuits heterogeneously integrated with advanced analog electrochemical random-access memory (ECRAM) hardware. It functions as a robust, compact, energy-efficient, and cost-effective in-memory-computing architecture that renders the edge-learning capability to robots navigating in complicated and evolving environments. To accomplish this target, the team will develop high-speed ECRAM devices and arrays incorporating novel nano-structured solid-state protonic electrolytes. Physics-based and experimentally verified device models will be established to correlate the material properties with ECRAM performances as part of the process design kit. ECRAMs will then be integrated with silicon peripheral circuits to form crossbar micro-architectures, which can mitigate impacts from ECRAM device non-idealities through the adoption of numerical format and precision scaling techniques. Meanwhile, custom simultaneous localization and mapping algorithms will be co-designed with the unique hardware attributes in consideration. Finally, the team will build a hybrid system composed of the ECRAM edge-learning accelerator and auxiliary silicon chips integrated on a circuit board for robot self-navigation. The goal is to achieve 20x higher energy and area efficiency compared to solutions based on conventional silicon technologies and von Neumann architecture.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.