Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The growing computational and memory demands of artificial intelligence (AI) require disruptive innovation in how information is represented, stored, communicated, and processed. By leveraging novel device modalities and compute-in-memory (CIM), in addition to analog dynamics and sparse communication inspired by the brain, neuromorphic computing offers a promising path toward improvements in the energy efficiency and scalability of current AI systems. But realizing this potential is not a matter of replacing one chip with another; rather, it requires a co-design effort, spanning new materials and non-volatile device structures, novel mixed-signal circuits and architectures, and learning algorithms tailored to the physics of these substrates. This article surveys the key limitations of classical complementary metal-oxide-semiconductor (CMOS) technology and outlines how such cross-layer neuromorphic approaches may overcome them.

翻译：经典计算正开始遭遇能效的根本性瓶颈。通过增加电路密度或优化标准半导体工艺等策略已无法解决这一挑战。人工智能日益增长的计算与存储需求，需要在信息表征、存储、通信与处理方式上实现颠覆性创新。通过利用新型器件模式、存内计算，以及受大脑启发的模拟动力学与稀疏通信技术，神经形态计算为提升当前人工智能系统的能效与可扩展性提供了一条有前景的路径。然而，要释放这一潜力，并非简单以芯片替换芯片，而是需要跨层级协同设计，涵盖新材料与非易失性器件结构、新型混合信号电路与架构，以及适配这些基板物理特性的学习算法。本文梳理了经典互补金属氧化物半导体技术的关键局限，并阐述了此类跨层级神经形态方法如何有望突破这些局限。