General-purpose LSM learning processor architecture and theoretically guided design space exploration
General-purpose LSM learning processor architecture and theoretically guided design space exploration
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通用LSM学习处理器架构和理论指导设计空间探索
DOI：
10.1109/biocas.2015.7348397
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Peng Li
中科院分区：
文献类型：
--
作者：
Qian Wang;Yingyezhe Jin;Peng Li
针对现实世界的模式识别问题，提出了一种集训练和识别于一体的通用液态机神经形态学习处理器。该架构由一个通用预处理器和一个或多个任务处理器组成。预处理器或存储库由具有固定突触权重的循环尖峰神经网络组成。任务处理器重量轻，由一组具有塑料权重的读出尖峰神经元组成，这些神经元通过生物学上合理的监督学习规则进行调节。重要的是，我们利用库的独特计算结构在同一学习处理器上高效地实现多个任务。提出了一种与真实学习性能密切相关的计算能力的新理论度量，以促进循环水库的快速设计空间探索。我们通过将四个识别任务映射到可重构的FPGA处理器平台来演示我们的处理器架构的应用。
This paper presents a general-purpose liquid state machine based neuromorphic learning processor with integrated training and recognition for real world pattern recognition problems. The proposed architecture consists of a generic preprocessor and one or multiple task processors. The pre-processor, or the reservoir, consists of a recurrent spiking neural network with fixed synaptic weights. Task processors are light weight and comprise a set of readout spiking neurons with plastic weights, which are tuned by a biologically plausible supervised learning rule. Importantly, we leverage the unique computational structure of the reservoir for highly efficient implementation of multiple tasks on the same learning processor. A novel theoretical measure of computational power, which is strongly correlated with the true learning performance, is proposed to facilitate fast design space exploration of the recurrent reservoir. We demonstrate the application of our processor architecture by mapping four recognition tasks onto a reconfigurable FPGA processor platform.