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.

本研究针对现有神经计算硬件架构的效率瓶颈，提出了一种使用阻变存储器件的新方法。通过利用这些器件独特的物理动态实现推理、学习和路由，研究发现即使在模拟物理的不理想性与噪声影响下，局部学习仍然具有适应性，为可扩展架构中有效路由与信贷分配问题提供了新解决方案。

类比炼金术：基于存储的推理、学习和路由的神经计算