为类脑硬件设计新编程范式，突破深度学习局限。
Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware
- 提出五项核心特征构建类脑编程框架
- 强调需用新抽象方法释放硬件潜力
- 适合硬件架构师与系统级开发者
类脑计算的价值取决于我们能否为其编程实现实际任务。当前类脑硬件多依赖从深度学习迁移而来的机器学习方法，但若能充分挖掘其能效优势与完整计算能力，其潜力远超深度学习。类脑编程必须区别于传统编程，需在思维模式上实现范式转变。本文通过概念分析，探讨类脑计算中的编程问题，挑战现有范式，提出更贴近硬件物理特性的框架。分析聚焦于五大基础特征，为对比现有编程方法与语言提供基准。通过回顾过往实践，本文倡导使用被忽视的技术，并呼吁发展更丰富的抽象机制，以有效操控这一新兴硬件类别。
原文摘要 · Abstract (English)
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.
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