CAREER: SHF: Bio-Inspired Microsystems for Energy-Efficient Real-Time Sensing, Decision, and Adaptation

当代人工智能系统通常不能真实的适应其环境，非常耗电，并且通常与底层硬件隔离开发。然而，生物智能已经解决了这些限制，节能，适应性强，并与底层基质良好整合。该项目以生物智能为指导原则，开发微电子系统，这些系统可以有效地感知环境中的物理信号，做出实时决策，并以最小的能源消耗进行适应和学习。通过从生物学中汲取灵感，该项目通过无缝集成记忆，计算和传感以及学习算法来模糊传感，计算和算法之间的区别。通过镜像这种生物模型，该项目旨在开发下一代自主智能系统，其应用范围从更智能的手机到改进的脑机接口。概述的教育活动将使与工业合作伙伴和历史悠久的黑人学院和大学的合作，使尖端的微电子教育，使国内劳动力，以满足国家的战略半导体需求。该项目共同设计持续学习算法与尖端，节能，利用铁电场效应晶体管（FeFET）形式的新兴器件来实现下一代、节能、自适应硬件的微电子设计，和学习利用FeFET作为可编程跨导的模拟到特征转换器前端系统将被设计为采集模拟输入并提取相关的学习特征。这些子系统将为下游基于FeFET的存储器计算（CIM）电路提供定制设计的电路，以缓解目前限制大多数CIM架构的模数转换器瓶颈。静态随机存取存储器将增强FeFET结构，以实现片上学习和动态可重构性。与底层硬件同步，定制的持续学习算法将与模数转换器和FeFET阵列共同设计，以赋予系统节能的弹性和适应性。使用所提出的方法设计的微电子系统可以看到广泛的应用，从脑机接口和植入式系统的盲波形分类的无线系统。为了验证该方法，将制造和测量集成电路。这些还将提供数据，进一步完善和校准用于性能评估和设计空间探索的软件模型。该项目将最终开发出对生物启发、节能、自主代理至关重要的组件。该奖项反映了NSF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，被认为值得支持。

Contemporary artificial intelligence systems typically do not adapt to their environment in real time, are very power hungry, and are typically developed in isolation from the underlying hardware. However, biological intelligence has addressed these limitations, being energy-efficient, adaptable, and well-integrated with the underlying substrate. Using biological intelligence as the guiding principle, this project develops microelectronic systems that can efficiently, sense physical signals from their environment, make real-time decisions, and adapt and learn with minimal energy usage. By drawing inspiration from biology, this project blurs the distinction between sensing, computation, and algorithm by seamless integration of memory, computing, and sensing and a learning algorithm. Through mirroring this biological model, the project seeks to develop the next generation autonomous intelligent systems with applications ranging from smarter cellphones to improved brain-machine interfaces. The outlined educational activities will enable collaboration with industrial partners and historically black colleges and universities to enable cutting edge microelectronic education to enable the domestic workforce to meet the strategic semiconductor needs of the Nation.The project co-designs continual learning algorithms with cutting-edge, energy-efficient, microelectronic designs that leverage emerging devices in the form of Ferroelectric Field-Effect Transistors (FeFETs) to enable next-generation, energy-efficient, adaptive hardware for sensing, decision-making, and learning. Analog-to-Feature converter front-end systems leveraging FeFETs as programmable transconductances will be designed to acquire analog input and extract pertinent learned features. These subsystems will feed downstream FeFET-based compute-in-memory (CIM) circuits with custom-designed circuits to alleviate the analog-to-digital converter bottleneck currently limiting most CIM architectures. Static Random Access Memory will augment FeFET structures to enable on-chip learning and dynamic reconfigurability. In lockstep with the underlying hardware, tailored continual learning algorithms will be co-designed with the analog-to-digital converters and the FeFET array to endow the system with energy-efficient resilience and adaptation. Microelectronic systems designed using the presented approach could see wide ranging applications from brain-computer-interfaces and implantable systems to blind waveform classification for wireless systems. To validate the approach, an integrated circuit will be fabricated and measured. These will also serve to provide data to further refine and calibrate software models for performance evaluation and design-space exploration. This project will ultimately develop components critical for biologically inspired, energy-efficient, autonomous agents.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.