论文标题
Pelenet：loihi的储层计算框架
PeleNet: A Reservoir Computing Framework for Loihi
论文作者
论文摘要
用于峰值神经网络的高级框架是快速原型制作和复杂算法开发的关键因素。在过去的几年中，对于传统计算机而出现了这样的框架，但是编程神经形态硬件仍然是一个挑战。通常需要低级编程，并了解有关神经形态芯片的硬件的知识。 Pelenet框架旨在简化神经形态硬件Loihi的储层计算。它是由英特尔（Intel）的NXSDK上面构建的，并用Python编写。该框架管理重量矩阵，参数和探针。特别是，它在几个内核和芯片上提供了网络的自动分布。因此，用户不面临技术细节，可以集中精力进行实验。
High-level frameworks for spiking neural networks are a key factor for fast prototyping and efficient development of complex algorithms. Such frameworks have emerged in the last years for traditional computers, but programming neuromorphic hardware is still a challenge. Often low level programming with knowledge about the hardware of the neuromorphic chip is required. The PeleNet framework aims to simplify reservoir computing for the neuromorphic hardware Loihi. It is build on top of the NxSDK from Intel and is written in Python. The framework manages weight matrices, parameters and probes. In particular, it provides an automatic and efficient distribution of networks over several cores and chips. With this, the user is not confronted with technical details and can concentrate on experiments.
