EFRI BRAID: Scalable-Learning Neuromorphics

人工智能领域的最新进展表明，通向更高智能的大脑启发系统的途径是通过显著增加网络规模。然而，这种相当渐进的方法面临着严峻的挑战。训练用于自然语言处理的最大机器学习模型需要数月的数据中心规模的计算，换句话说，需要巨大的精力、时间和成本。例如，由于算法和硬件的改进，用于改进的储备似乎是有限的，最重要的是，半导体技术的规模不再能推动进一步的进步。此外，最先进的模型依赖于拥有海量训练数据的线下训练，即无法持续实时学习。这样的挑战自然带来了对生物神经网络的更多关注，生物神经网络是在非常节能的“大脑”硬件上运行的卓越、灵活和自适应智能的活生生的证明。尖峰神经网络理论、生物学上最可信的模型以及用密集的新兴记忆设备实现的神经形态电路的最新发展带来了令人兴奋的机会。拟议的项目旨在利用这些进展并解决最紧迫的挑战，为具有实用(健壮、快速、廉价)学习能力的人脑规模的神经形态系统开发可扩展的算法和硬件。该项目将使神经形态系统在许多实际应用中具有直接重要性，包括自动机器人和车辆，以及生物医学，包括便携式和个人医疗设备。此外，拟议研究的广泛的算法到系统的性质为高中生、本科生和研究生提供了有吸引力的机会来探索新的研究并接触到新兴的神经形态计算领域。拟议的项目将通过利用参与大学的计划开展外展活动，重点是吸引少数族裔学生。我们研究的主要特点是硬件友好的本地学习算法、持续在线“一次”学习的框架，以及容忍变化的内存计算硬件电路。具体地说，在算法方面，我们专注于具有生物学上可信的尖峰频率适应神经元的递归尖峰神经网络。我们将建立在我们的团队成员最近提出的本地学习算法的基础上，这些算法通过突触可塑性促进更长时间的学习。这些算法将进一步扩展，以支持持续学习，并使用神经结构搜索技术与硬件共同优化。在硬件方面，重点是利用模拟内存计算的混合神经形态电路。关键的硬件挑战，如网络复杂性的扩展和稳健的原位学习的实施，将通过利用实现学习算法的固定值权重的超高密度交叉点设备和具有短期和长期可塑性的新型容忍变化的记忆突触来解决。算法和硬件电路将被整体集成到学习到学习尖峰神经网络框架中。这样的框架实现了两种学习--一种是模仿发展性学习的缓慢增量学习，另一种是利用网络动态的快速“一次性”学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Recent advances in the field of artificial intelligence suggest that the route toward higher-intelligence brain-inspired systems is via significantly increasing network size. However, such a rather evolutionary approach faces dire challenges. Training the largest machine learning models for natural language processing requires months of data-center-scale computations, in other words, enormous energy, time, and cost. Reserves for improvements, e.g., due to the refinement of algorithms and hardware, seem limited, and most importantly, further advances could no longer be fueled by semiconductor technology scaling. Additionally, state-of-the-art models rely on offline training with vast amount of training data, i.e., are not capable of continual real-time learning. Such challenges naturally bring more attention to the biological neural networks, which are living proof of superior, agile, and adaptive intelligence running on very energy-efficient “brain” hardware. Exciting opportunities are presented by recent developments in the theory of spiking neural networks, the most biologically plausible models, and neuromorphic circuits implemented with dense emerging memory devices. The proposed project aims to capitalize on these advances and address the most pressing challenges to develop scalable algorithms and hardware for human-brain-scale neuromorphic systems with practically useful (robust, fast, inexpensive) learning capabilities. The project will enable neuromorphic systems of immediate importance for many practical applications, including autonomous robots and vehicles, and biomedicine, including portable and personal medical devices. Furthermore, the broad algorithm-to-system nature of the proposed research provides attractive opportunities for high-school, undergraduate, and graduate students to explore novel research and get exposed to the emerging field of neuromorphic computing. The proposed project will pursue outreach activities by leveraging programs at participating universities, with a particular focus on attracting minority students.The key features of our research are hardware-friendly local learning algorithms, a framework for continual online “one-shot” learning, and variation-tolerant in-memory computing hardware circuits. Specifically, on the algorithmic front, we focus on recurrent spiking neural networks with biologically-plausible spike frequency adaptation neurons. We will build on local learning algorithms recently proposed by our team members that facilitate learning over longer time scales via synaptic plasticity. These algorithms will be further extended to support continual learning and co-optimized with the hardware using neural architecture search techniques. On the hardware front, the focus is on hybrid neuromorphic circuits that take advantage of analog in-memory computing. Critical hardware challenges, such as the scaling of network complexity and implementation of robust in-situ learning, will be addressed by utilizing ultra-high-density crosspoint devices implementing fixed-value weights of the learning algorithms and novel variation-tolerant memristive synapses featuring both short-term and long-term plasticity. Algorithms and hardware circuits will be holistically integrated into the learning-to-learn spiking neural network framework. Such a framework enables two kinds of learning – a slow incremental one mimicking developmental learning and fast “one-shot” learning utilizing network dynamics.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.