The unprecedented advancement of artificial intelligence has placed immense demands on computing hardware, but traditional silicon-based semiconductor technologies are approaching their physical and economic limit, prompting the exploration of novel computing paradigms. Memristor offers a promising solution, enabling in-memory analog computation and massive parallelism, which leads to low latency and power consumption. This manuscript reviews the current status of memristor-based machine learning accelerators, highlighting the milestones achieved in developing prototype chips, that not only accelerate neural networks inference but also tackle other machine learning tasks. More importantly, it discusses our opinion on current key challenges that remain in this field, such as device variation, the need for efficient peripheral circuitry, and systematic co-design and optimization. We also share our perspective on potential future directions, some of which address existing challenges while others explore untouched territories. By addressing these challenges through interdisciplinary efforts spanning device engineering, circuit design, and systems architecture, memristor-based accelerators could significantly advance the capabilities of AI hardware, particularly for edge applications where power efficiency is paramount.

翻译：人工智能的飞速发展对计算硬件提出了巨大需求，然而传统硅基半导体技术正逼近其物理与经济极限，这促使人们探索新型计算范式。忆阻器提供了一种前景广阔的解决方案，它支持内存内模拟计算与大规模并行处理，从而实现低延迟与低功耗。本文综述了基于忆阻器的机器学习加速器的发展现状，重点介绍了原型芯片研发中取得的里程碑进展——这些芯片不仅能加速神经网络推理，还能处理其他机器学习任务。更重要的是，本文探讨了我们对该领域当前关键挑战的看法，包括器件差异性、高效外围电路的需求，以及系统级的协同设计与优化。同时，我们也展望了未来可能的发展方向，其中部分方向致力于解决现有挑战，另一些则探索尚未涉足的领域。通过融合器件工程、电路设计与系统架构的跨学科努力来应对这些挑战，基于忆阻器的加速器有望显著提升人工智能硬件的能力，特别是在能效至关重要的边缘计算应用领域。