FET: Small: Ferroelectric Transistor based Spiking Neural Networks with Adaptive Learning for Edge AI: from Devices to Algorithms

将人工智能（AI）纳入电子系统已被广泛认为是一些新兴应用的关键推动因素之一。然而，最先进的人工智能系统的能源效率和学习能力与人类大脑所能达到的水平相去甚远。本研究进行了跨层探索，包括新设备、低功耗神经网络和新的学习方案。该探索将利用具有内在神经模拟特性的铁电场效应晶体管（FeFET）技术来实现节能的神经硬件和自适应学习。低功耗硬件解决方案和自适应学习算法有可能影响计算机辅助诊断、机器人、语音/面部识别和数据分类等关键应用，从而直接惠及医疗保健、国防、安全等领域。此外，节电应该转化为边缘设备的更长的电池寿命，以及可穿戴健康监测平台等应用的节能数据处理。该项目将利用普渡大学的外展项目，并开发一个本科生暑期研究体验项目（REU），让本科生和少数民族学生参与该项目。该项目的广泛性将为本科生提供机会，让他们了解基于新兴技术的人工智能领域。尖峰神经网络（snn）由于具有自学习能力，有望为人工智能系统引入适应性学习，但准确性较低。提高SNN的精度和性能不仅需要支持自适应终身学习的新颖学习机制，而且还需要一种本质上适合低功耗可扩展硬件的技术。为了满足这一关键需求，该项目将对基于多域场效应晶体管的snn进行全面的器件到算法探索。主要目标包括(a)使用fefet设计低功耗神经元和突触，以及(b)利用神经模拟装置的独特属性开发自适应和顺序学习算法。为了使fefet具有生物似是而非的特性，将进行基于物理的器件优化，以利用铁电体的多域效应和域动力学。为了促进跨层探索，将开发一个设备到系统的仿真框架，捕获神经元和突触的丰富动态，它们在SNN中的相互作用，以及新的学习算法对系统性能/准确性的影响。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，被认为值得支持。

Incorporating artificial intelligence (AI) in electronic systems has been widely recognized as one of the key enablers for several emerging applications. However, the energy efficiency and the learning capabilities of state-of-the-art AI systems is far from that achievable by human brains. This research undertakes a cross-layer exploration spanning novel devices, low-power neural networks, and new learning schemes. The exploration will exploit Ferroelectric Field Effect Transistor (FeFET) technology with intrinsic neuro-mimetic features to achieve energy-efficient neural hardware and adaptable learning. The low-power hardware solutions and adaptive-learning algorithms have the potential to impact critical applications such as computer-aided diagnosis, robotics, speech/face recognition, and data classification, thereby directly benefiting areas such as healthcare, defense, security etc. Moreover, power savings should translate to longer battery life for edge devices and energy-efficient data processing for applications like wearable health-monitoring platforms. The project will leverage outreach programs at Purdue University and develop a summer Research Experiences for Undergraduates (REU) program to involve undergraduates and minority students in the project. The broad nature of this project will provide opportunity for undergraduate students to get introduced to the field of AI based on emerging technologies.Spiking Neural Networks (SNNs), due to their self-learning capabilities, show promise in introducing adaptability in learning for AI systems, but suffer from low accuracy. Improving SNN accuracy and performance not only necessitates novel learning mechanisms that support adaptable lifelong learning, but also an intrinsically suitable technology for low-power scalable hardware. To address this critical need, this project will carry out a comprehensive devices-to-algorithms exploration of multi-domain FeFET based SNNs. The main objectives include (a) design of low-power neurons and synapses using FeFETs, and (b) development of adaptive and sequential learning algorithms utilizing the unique attributes of the neuro-mimetic devices. To enable bio-plausible features in FeFETs, physics-based device optimization will be carried out to utilize the multi-domain effects and domain dynamics of ferroelectrics. To facilitate cross-layer exploration, a devices-to-systems simulation framework will be developed capturing the rich dynamics of the neurons and synapses, their interactions in an SNN, and the impact of new learning algorithms on system performance/accuracy.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.