FPGA中硬件支持的储备池计算的高效设计
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs
摘要：
在这项工作中，我们提出了一种新的方法，对有效的优化和实施的储备池计算硬件减少所需的领域专家知识和优化工作。首先，我们通过线性自编码器使水库输入掩码适应数据的结构。因此，我们将降维和维数扩展的优势，实现了传统的算法有效的线性代数程序的主成分分析。第二，我们采用进化启发遗传算法技术，从而在一个非常有效的优化油藏动态与显着减少的数量相比，穷举搜索的评价。我们说明了所谓的单节点储备池计算架构，特别适合在超高速硬件实现的方法。这两种方法的组合以及由此产生的硬件系统的性能优化所需的时间的减少建立了一种朝向能够自适应以最佳地解决特定问题的机器学习硬件的策略。我们证实了这些原则的有效性建设储备池计算硬件的基础上，现场可编程门阵列。
英文摘要：
In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort. First, we adapt the reservoir input mask to the structure of the data via linear autoencoders. We therefore incorporate the advantages of dimensionality reduction and dimensionality expansion achieved by conventional algorithmically efficient linear algebra procedures of principal component analysis. Second, we employ evolutionary-inspired genetic algorithm techniques resulting in a highly efficient optimization of reservoir dynamics with dramatically reduced number of evaluations comparing to exhaustive search. We illustrate the method on the so-called single-node reservoir computing architecture, especially suitable for implementation in ultrahigh-speed hardware. The combination of both methods and the resulting reduction of time required for performance optimization of a hardware system establish a strategy towards machine learning hardware capable of self-adaption to optimally solve specific problems. We confirm the validity of those principles building reservoir computing hardware based on a field-programmable gate array.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
