论文标题
在加速神经形态硬件上建模的一种可扩展方法
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
论文作者
论文摘要
神经形态系统为扩大计算研究的探索空间提供了机会。但是，团结效率和可用性通常是具有挑战性的。这项工作介绍了Brainscales-2系统这项努力的软件方面，这是一种基于物理建模的混合加速神经形态硬件体系结构。我们介绍了Brainscales-2操作系统的关键方面：实验工作流，API分层，软件设计和平台操作。我们提出用例，以讨论和得出有关软件的要求并展示实现的要求。重点在于新型系统和软件功能，例如多室神经元，快速重新配置用于硬件式培训，嵌入式处理器的应用程序，非加速操作模式，交互式平台访问以及可持续的硬件/软件/软件共同开发。最后，我们讨论了有关硬件扩展，系统可用性和效率的进一步发展。
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability and efficiency.
