Neuromorphic Design Using Reward-based STDP Learning on Event-Based Reconfigurable Cluster Architecture
Neuromorphic Design Using Reward-based STDP Learning on Event-Based Reconfigurable Cluster Architecture
复制标题
在基于事件的可重构集群架构上使用基于奖励的 STDP 学习进行神经形态设计
DOI：
10.1145/3477145.3477151
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
W. Luk
中科院分区：
文献类型：
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作者：
Mahyar Shahsavari;David B. Thomas;Andrew D. Brown;W. Luk
神经形态计算系统模拟尖峰神经网络，用于研究生物神经网络的功能，以及用于应用工程，如机器人学、模式识别和机器学习。本文提出了一种基于异步事件硬件平台的神经形态系统。我们给出了在我们的异步硬件平台上实现尖峰网络的三种算法。我们还讨论了同步和消息传递成本之间的不同权衡。作为一种在线学习算法，提出了一种称为报酬调制STDP的强化学习方法。我们在我们设计的体系结构中使用6000个并发硬件线程对系统性能进行了评估，并演示了如何扩展到拥有多达200万个神经元和4亿个突触的网络。我们的体系结构的性能也与现有的神经形态平台进行了比较，显示出在x86机器上比Brian模拟器快20倍，在48芯片Spinnaker节点上快16倍。
Neuromorphic computing systems simulate spiking neural networks that are used for research into how biological neural networks function, as well as for applied engineering such as robotics, pattern recognition, and machine learning. In this paper, we present a neuromorphic system based on an asynchronous event-based hardware platform. We represent three algorithms for implementing spiking networks on our asynchronous hardware platform. We also discuss different trade-offs between synchronisation and messaging costs. A reinforcement learning method known as Reward-modulated STDP is presented as an online learning algorithm in the network. We evaluate the system performance in a single box of our designed architecture using 6000 concurrent hardware threads and demonstrate scaling to networks with up to 2 million neurons and 400 million synapses. The performance of our architecture is also compared to existing neuromorphic platforms, showing a 20 times speed-up over the Brian simulator on an x86 machine, and a 16 times speed-up over a 48-chip SpiNNaker node.