Neuromorphic computing (NMC) is increasingly viewed as a low-power alternative to conventional von Neumann architectures such as central processing units (CPUs) and graphics processing units (GPUs), however the computational value proposition has been difficult to define precisely. Here, we explain how NMC should be seen as general-purpose and programmable even though it differs considerably from a conventional stored-program architecture. We show that the time and space scaling of NMC is equivalent to that of a theoretically infinite processor conventional system, however the energy scaling is significantly different. Specifically, the energy of conventional systems scales with absolute algorithm work, whereas the energy of neuromorphic systems scales with the derivative of algorithm state. The unique characteristics of NMC architectures make it well suited for different classes of algorithms than conventional multi-core systems like GPUs that have been optimized for dense numerical applications such as linear algebra. In contrast, the unique characteristics of NMC make it ideally suited for scalable and sparse algorithms whose activity is proportional to an objective function, such as iterative optimization and large-scale sampling (e.g., Monte Carlo).

翻译：神经形态计算（NMC）日益被视为传统冯·诺依曼架构（如中央处理器CPU和图形处理器GPU）的一种低功耗替代方案，然而其计算价值主张一直难以精确定义。本文阐释了NMC应如何被视为通用且可编程的计算范式，尽管其与传统存储程序架构存在显著差异。我们证明NMC在时间与空间缩放方面等价于理论上的无限处理器传统系统，但其能量缩放特性存在本质区别。具体而言，传统系统的能量消耗随算法绝对工作量缩放，而神经形态系统的能量消耗则随算法状态导数的变化而缩放。NMC架构的独特特性使其特别适用于与GPU等多核传统系统不同的算法类别——后者已针对线性代数等密集型数值计算进行优化。相比之下，NMC的独特性质使其天然适配于活动强度与目标函数成正比的、可扩展的稀疏算法，例如迭代优化与大规模采样（如蒙特卡洛方法）。