用忆阻器实现存内计算，让神经网络更省电高效
Analog Alchemy: Neural Computation with In-Memory Inference, Learning and Routing
- 利用忆阻器物理特性实现推理、学习与路由一体化
- 在非理想模拟器件中仍实现局部学习自适应与误差修正
- 适合追求能效比的类脑计算系统开发者
随着神经计算推动人工智能发展，重构理想的神经硬件成为新前沿。传统冯·诺依曼架构将内存与计算分离，造成能效瓶颈，与生物大脑不符。本文探索基于忆阻器的替代方案，利用器件独特的物理动态实现推理、学习与路由。基于梯度学习原则，确定需实现的功能，并分析连接组学原理以优化布线。尽管存在物理噪声与非理想性，仍验证了局部学习在忆阻基底上的可适应性，提出新型材料堆叠与电路模块，解决信用分配问题，并实现模拟交叉阵列间的高效路由，为可扩展架构提供硬件支持。
原文摘要 · Abstract (English)
As neural computation is revolutionizing the field of Artificial Intelligence (AI), rethinking the ideal neural hardware is becoming the next frontier. Fast and reliable von Neumann architecture has been the hosting platform for neural computation. Although capable, its separation of memory and computation creates the bottleneck for the energy efficiency of neural computation, contrasting the biological brain. The question remains: how can we efficiently combine memory and computation, while exploiting the physics of the substrate, to build intelligent systems? In this thesis, I explore an alternative way with memristive devices for neural computation, where the unique physical dynamics of the devices are used for inference, learning and routing. Guided by the principles of gradient-based learning, we selected functions that need to be materialized, and analyzed connectomics principles for efficient wiring. Despite non-idealities and noise inherent in analog physics, I will provide hardware evidence of adaptability of local learning to memristive substrates, new material stacks and circuit blocks that aid in solving the credit assignment problem and efficient routing between analog crossbars for scalable architectures.
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