Super-TinyML aims to optimize machine learning models for deployment on ultra-low-power application domains such as wearable technologies and implants. Such domains also require conformality, flexibility, and non-toxicity which traditional silicon-based systems cannot fulfill. Printed Electronics (PE) offers not only these characteristics, but also cost-effective and on-demand fabrication. However, Neural Networks (NN) with hundreds of features -- often necessary for target applications -- have not been feasible in PE because of its restrictions such as limited device count due to its large feature sizes. In contrast to the state of the art using fully parallel architectures and limited to smaller classifiers, in this work we implement a super-TinyML architecture for bespoke (application-specific) NNs that surpasses the previous limits of state of the art and enables NNs with large number of parameters. With the introduction of super-TinyML into PE technology, we address the area and power limitations through resource sharing with multi-cycle operation and neuron approximation. This enables, for the first time, the implementation of NNs with up to $35.9\times$ more features and $65.4\times$ more coefficients than the state of the art solutions.

翻译：超微型机器学习旨在优化机器学习模型，以部署于可穿戴技术与植入体等超低功耗应用领域。此类领域同时要求共形性、柔性与无毒性，这是传统硅基系统无法满足的。印刷电子技术不仅具备这些特性，还能实现低成本、按需制造。然而，目标应用通常需要具备数百个特征的神经网络，由于印刷电子技术存在特征尺寸大导致器件数量受限等制约，此类网络一直难以在该技术中实现。与当前采用全并行架构且仅限于较小分类器的技术方案不同，本研究实现了一种面向定制化（应用专用）神经网络的超微型机器学习架构，该架构突破了现有技术的先前限制，实现了具有大量参数的神经网络。通过将超微型机器学习引入印刷电子技术，我们借助多周期操作的资源共享与神经元近似方法，解决了面积与功耗的限制。这首次实现了神经网络的特征数量与系数数量分别达到现有技术方案的35.9倍与65.4倍。