Neuromorphic computing aims to incorporate lessons from studying biological nervous systems in the design of computer architectures. While existing approaches have successfully implemented aspects of those computational principles, such as sparse spike-based computation, event-based scalable learning has remained an elusive goal in large-scale systems. However, only then the potential energy-efficiency advantages of neuromorphic systems relative to other hardware architectures can be realized during learning. We present our progress implementing the EventProp algorithm using the example of the BrainScaleS-2 analog neuromorphic hardware. Previous gradient-based approaches to learning used "surrogate gradients" and dense sampling of observables or were limited by assumptions on the underlying dynamics and loss functions. In contrast, our approach only needs spike time observations from the system while being able to incorporate other system observables, such as membrane voltage measurements, in a principled way. This leads to a one-order-of-magnitude improvement in the information efficiency of the gradient estimate, which would directly translate to corresponding energy efficiency improvements in an optimized hardware implementation. We present the theoretical framework for estimating gradients and results verifying the correctness of the estimation, as well as results on a low-dimensional classification task using the BrainScaleS-2 system. Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances. It also suggests the feasibility of a full on-device implementation of the algorithm that would enable scalable, energy-efficient, event-based learning in large-scale analog neuromorphic hardware.

翻译：神经形态计算旨在将生物神经系统研究中的经验融入计算机架构设计。现有方法已成功实现了诸如稀疏脉冲计算等计算原理的某些方面，但在大规模系统中，基于事件的可扩展学习仍是一个难以实现的目标。然而，唯有如此，神经形态系统相对于其他硬件架构的潜在能效优势才能在学习过程中得以体现。我们以BrainScaleS-2模拟神经形态硬件为例，展示了实现EventProp算法的进展。以往基于梯度的学习方法采用"替代梯度"和密集观测采样，或受限于对底层动力学和损失函数的假设。相比之下，我们的方法仅需从系统获取脉冲时间观测值，同时能够以系统性的方式纳入其他系统观测变量（如膜电压测量）。这使梯度估计的信息效率提升了一个数量级，在优化的硬件实现中可直接转化为相应的能效提升。我们提出了梯度估计的理论框架，验证了估计正确性的实验结果，以及使用BrainScaleS-2系统完成的低维分类任务结果。基于此项工作，有望在大规模神经形态硬件中实现可扩展的梯度估计——因为在此类场景中，对系统状态进行连续测量既不可行且能效低下。该研究还表明，算法完全在设备上实现是可行的，从而能够在大规模模拟神经形态硬件中实现可扩展、高能效、基于事件的学习。