The value of brain-inspired neuromorphic computers critically depends on our ability to program them for relevant tasks. Currently, neuromorphic hardware often relies on machine learning methods adapted from deep learning. However, neuromorphic computers have potential far beyond deep learning if we can only harness their energy efficiency and full computational power. Neuromorphic programming will necessarily be different from conventional programming, requiring a paradigm shift in how we think about programming. This paper presents a conceptual analysis of programming within the context of neuromorphic computing, challenging conventional paradigms and proposing a framework that aligns more closely with the physical intricacies of these systems. Our analysis revolves around five characteristics that are fundamental to neuromorphic programming and provides a basis for comparison to contemporary programming methods and languages. By studying past approaches, we contribute a framework that advocates for underutilized techniques and calls for richer abstractions to effectively instrument the new hardware class.

翻译：脑启发神经形态计算机的价值，关键取决于我们为其编程以执行相关任务的能力。目前，神经形态硬件通常依赖于从深度学习领域借鉴而来的机器学习方法。然而，神经形态计算机的潜力远不止于深度学习，前提是我们能够充分利用其能效和全部计算能力。神经形态编程必然不同于传统编程，它要求我们在编程思维方式上进行范式转变。本文在神经形态计算的背景下，对编程进行了概念性分析，挑战了传统范式，并提出了一个更贴近此类系统物理复杂性的框架。我们的分析围绕神经形态编程的五个基本特征展开，并以此为基础与当代编程方法和语言进行比较。通过研究过往方法，我们提出了一个框架，该框架倡导利用未充分开发的技术，并呼吁建立更丰富的抽象层次，以有效驾驭这类新型硬件。