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Design exploration of ASIP architectures for the K-Nearest Neighbor machine-learning algorithm

机译：K最近邻机器学习算法的ASIP架构设计探索

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Increasingly, machine-learning algorithms are playing an important role in the context of embedded and real-time systems. Applications such as wireless sensor networks, security, and commercial enterprises are increasingly relying on machine-learning algorithms to efficiently make predictive decisions based on the large volumes of data these systems collect. Therefore, there is a need to accelerate the runtime of these algorithms, especially for real-time applications. In this paper, we propose several Application Specific Instruction Processor (ASIP) architectures for the K-Nearest Neighbor (KNN) classification algorithm. Each ASIP is developed using Cadence Tensilica tools and represents a tightly-coupled architecture. Our experimental results, based on several benchmarks, show that proposed ASIPs achieve speedups of 86×-650× over the original software implementation.
机译：机器学习算法越来越多地在嵌入式和实时系统的环境中发挥重要作用。无线传感器网络，安全性和商业企业等应用程序越来越依赖于机器学习算法来基于这些系统收集的大量数据有效地做出预测性决策。因此，需要加快这些算法的运行时间，尤其是对于实时应用程序。在本文中，我们为K最近邻居（KNN）分类算法提出了几种专用指令处理器（ASIP）架构。每个ASIP都是使用Cadence Tensilica工具开发的，代表了紧密耦合的体系结构。我们基于几个基准的实验结果表明，与原始软件实现相比，拟议的ASIP实现了86×-650×的加速。

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