The development of power-efficient Machine Learning Hardware is of high importance to provide Artificial Intelligence (AI) characteristics to those devices operating at the Edge. Unfortunately, state-of-the-art data-driven AI techniques such as deep learning are too costly in terms of hardware and energy requirements for Edge Computing (EC) devices. Recently, Cellular Automata (CA) have been proposed as a feasible way to implement Reservoir Computing (RC) systems in which the automaton rule is fixed and the training is performed using a linear regression model. In this work we show that Reservoir Computing based on CA may arise as a promising AI alternative for devices operating at the edge due to its intrinsic simplicity. For this purpose, a new low-power CA-based reservoir hardware is proposed and implemented in a FPGA (known as ReCA circuitry). The use of Elementary Cellular Automata (ECA) is able to further simplify the RC structure to implement a power efficient AI system suitable to be implemented in EC applications. Experiments have been conducted on the well-known MNIST handwritten digits database, obtaining competitive results in terms of processing time, circuit area, power and inference accuracy.

中文翻译：

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具有基本元胞自动机的节能模式识别硬件

开发节能机器学习硬件对于为在边缘运行的设备提供人工智能 (AI) 特性非常重要。不幸的是，就边缘计算 (EC) 设备的硬件和能源要求而言，最先进的数据驱动 AI 技术（例如深度学习）成本太高。最近，已提出元胞自动机 (CA) 作为实现水库计算 (RC) 系统的可行方法，其中自动机规则是固定的，并且使用线性回归模型进行训练。在这项工作中，我们表明基于 CA 的 Reservoir Computing 由于其内在的简单性，可能会成为边缘运行设备的有前途的 AI 替代方案。为此，提出了一种新的基于 CA 的低功耗水库硬件，并在 FPGA（称为 ReCA 电路）中实现。基本元胞自动机 (ECA) 的使用能够进一步简化 RC 结构，以实现适合在 EC 应用中实现的高能效 AI 系统。在著名的 MNIST 手写数字数据库上进行了实验，在处理时间、电路面积、功率和推理精度方面获得了具有竞争力的结果。