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首页> 外文期刊>International journal of machine learning and cybernetics >Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on- chip training
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Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on- chip training

机译：新型自适应墨滴扩散算子的数字硬件实现及其在建模，分类和片上训练中的应用

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In artificial intelligence (AI), proposing an efficient algorithm with an appropriate hardware implementation has always been a challenge because of the well-accepted fact that AI hardware implementations should ideally be comparable to biological systems in terms of hardware area. Active learning method (ALM) is a fuzzy learning algorithm inspired by human brain computations. Unlike traditional algorithms, which employ complicated computations, ALM tries to model human brain computations using qualitative and behavioral descriptions of the problem. The main computational engine in ALM is the ink drop spread (IDS) operator, but this operator imposes high memory requirements and computational costs, making the ALM algorithm and its hardware implementation unsuitable for some of the applications. This paper proposes an adaptive alternative method for implementing the IDS operator; a method which results in a marked reduction in the algorithm's computational complexity and in the amount of memory required and hardware. To check its validity and performance, the method was used to carry out modeling and pattern classification tasks. This paper used challenging and real-world datasets and compared with well-known algorithms (adaptive neuro-fuzzy inference system and multi-layer perceptron) in software simulation and hardware implementation. Compared to traditional implementations of the ALM algorithm and other learning algorithms, the proposed FPGA implementation offers higher speed, less hardware, and improved performance, thus facilitating real-time application. Our ultimate goal in this paper was to present a hardware implementation with an on-chip training that allows it to adapt to its environment without dependency on the host system (on-chip learning).
机译：在人工智能（AI）中，提出一种具有适当硬件实现方式的高效算法一直是一项挑战，因为人们公认的事实是，AI硬件实现方式在硬件方面应该与生物系统相当。主动学习方法（ALM）是一种受人脑计算启发的模糊学习算法。与采用复杂计算的传统算法不同，ALM尝试使用对问题的定性和行为描述来对人脑计算进行建模。 ALM中的主要计算引擎是墨滴散布（IDS）运算符，但是该运算符要求很高的内存要求和计算成本，这使得ALM算法及其硬件实现不适用于某些应用程序。本文提出了一种实现IDS运算符的自适应替代方法。一种导致算法的计算复杂度以及所需的内存和硬件数量显着减少的方法。为了检查其有效性和性能，该方法用于执行建模和模式分类任务。本文使用具有挑战性的和现实世界的数据集，并在软件仿真和硬件实现中与著名的算法（自适应神经模糊推理系统和多层感知器）进行了比较。与ALM算法和其他学习算法的传统实现相比，拟议的FPGA实现提供了更高的速度，更少的硬件以及更高的性能，从而促进了实时应用。本文的最终目标是提供一种经过芯片培训的硬件实施方案，使之能够适应环境而不依赖主机系统（片上学习）。

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