State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, 350002, Fuzhou, Fujian, P. R. China.
Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, P. R. China.
Nat Commun. 2023 Jan 28;14(1):468. doi: 10.1038/s41467-023-36205-9.
In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.
传感器内多任务学习不仅是生物视觉的关键优势，也是人工智能的主要目标。然而，传统的硅基视觉芯片存在较大的时间/能量开销。此外，传统的深度学习模型的训练在边缘设备上既不可扩展也不经济。在这里，提出了一种材料-算法协同设计，以模拟人类视网膜和经济实惠的学习范例。基于具有高效激子解离和空间电荷传输特性的瓶刷状半导体 p-NDI，开发了一种基于晶体管的可穿戴式动态传感器内储层计算系统，该系统在不同任务上表现出出色的可分离性、记忆消退和回声状态特性。与忆阻器有机二极管上的“读出功能”相结合，RC 可识别手写字母和数字，并分别以 98.04%、88.18%和 91.76%的准确率对不同的服装进行分类（高于所有报道的有机半导体）。除了 2D 图像，RC 的时空动力学自然可以提取基于事件的视频的特征，以 98.62%的准确率对 3 种手势进行分类。此外，计算成本明显低于传统的人工神经网络。这项工作为经济高效的光子神经形态系统提供了有前途的材料-算法协同设计。
