Abstract
Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.
Abstract (translated)
Memristor-based神经网络为人工智能(AI)提供了卓越的能源效率平台，当与能量收集器配对使用时，可能实现自驱动运行。然而，大多数Memristor-based网络依赖于模拟存储器计算，因此需要稳定且精确的电源供应，这与天生不稳定且可靠的能量收集器不兼容。在这项工作中，我们制备了一个由32,768个Memristor组成的二进制化神经网络，由小型优化应用于边缘应用的宽频gap太阳能电池驱动。我们的电路采用了一种 resilient 数字近记忆计算方法，包括互补编程的Memristor和逻辑集成sense放大器。这种设计消除了补偿或校准的需求，在各种条件下有效运行。在高光照条件下，电路实现了与实验室电源供应相当的认知性能。在低光照条件下，它仍然有效，但精度略有降低，无缝过渡到近似计算模式。通过图像分类神经网络模拟，我们证明，在低光照条件下错误分类的图像主要是难以分类的情况。我们的方法为自驱动AI和用于健康、安全和环境监测各种应用的智能传感器奠定了基础。
URL
https://arxiv.org/abs/2305.12875
