Edge artificial intelligence or edge intelligence is an ever-growing research area due to the current popularization of the Internet of Things. Unfortunately, incorporation of artificial intelligence (AI) in smart devices operating at the edge is a challenging task due to the power-hungry characteristics of deep learning implementations, such as convolutional neural networks (CNNs). As a feasible alternative, reservoir computing (RC) has attracted a lot of attention in the field of machine learning due to its promising performance in a wide range of applications. In this work, we propose a simple hardware-optimized circuit design of RC systems presenting high energy-efficiency capacities that fulfill the low power requirements of edge intelligence applications. As a proof of concept, we used the proposed design for the implementation of a low-power audio event detection (AED) application in FPGA. The measurements and simulation results obtained show that the proposed approach may provide significant accuracy with the advantage of presenting ultra-low-power characteristics (the energy efficiency estimated is below the microjoule per inference). These results make the proposed system optimal for edge intelligence applications in which energy efficiency and accuracy are the key issues.

由于物联网的当前普及，边缘人工智能或边缘智能是一个不断发展的研究领域。不幸的是，由于深度学习实现（如卷积神经网络（CNN））的耗电特性，将人工智能（AI）集成到在边缘运行的智能设备中是一项艰巨的任务。作为一种可行的替代方案，储层计算（RC）由于其在广泛应用中的有希望的性能而在机器学习领域引起了很多关注。在这项工作中，我们提出了一种简单的硬件优化的RC系统电路设计，该系统具有高能效能力，可以满足边缘智能应用的低功耗要求。作为概念证明，我们使用提出的设计在FPGA中实现低功耗音频事件检测（AED）应用。获得的测量结果和仿真结果表明，所提出的方法可以提供非常低的精度，并且具有呈现超低功率特性的优势（每次推断估计的能量效率均低于微焦耳）。这些结果使所提出的系统最适合边缘智能应用，在这些应用中，能源效率和准确性是关键问题。