Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low integration density pose design challenges that hinder their practicality. In this work, we conduct a holistic exploration of printed neural network accelerators, starting from the analog-to-digital interface - a major area and power sink for sensor processing applications - and extending to networks of ternary neurons and their implementation. We propose bespoke ternary neural networks using approximate popcount and popcount-compare units, developed through a multi-phase evolutionary optimization approach and interfaced with sensors via customizable analog-to-binary converters. Our evaluation results show that the presented designs outperform the state of the art, achieving at least 6x improvement in area and 19x in power. To our knowledge, they represent the first open-source digital printed neural network classifiers capable of operating with existing printed energy harvesters.

翻译：印刷电子技术具有超低制造成本和按需制造柔性硬件的潜力。然而，其固有的较大特征尺寸和较低集成密度所带来的显著约束构成了设计挑战，阻碍了其实用化。本研究对印刷神经网络加速器进行了系统性探索，从模拟-数字接口（传感器处理应用中面积与功耗的主要消耗环节）出发，延伸至三值神经元网络及其实现。我们提出了采用近似popcount与popcount-compare单元的定制化三值神经网络，该网络通过多阶段进化优化方法开发，并可通过可定制模拟-二进制转换器与传感器对接。评估结果表明，所提出的设计在性能上超越了现有技术，实现了至少6倍的面积优化和19倍的功耗降低。据我们所知，这是首个能够与现有印刷能量收集器协同工作的开源数字印刷神经网络分类器。