With the advancement of deep learning and neural networks, the computational demands for applications in wearable devices have grown exponentially. However, wearable devices also have strict requirements for long battery life, low power consumption, and compact size. In this work, we propose a scalable optoelectronic computing system based on an integrated optical convolution acceleration core. This system enables high-precision computation at the speed of light, achieving 7-bit accuracy while maintaining extremely low power consumption. It also demonstrates peak throughput of 3.2 TOPS (tera operations per second) in parallel processing. We have successfully demonstrated image convolution and the typical application of an interactive first-person perspective gesture recognition application based on depth information. The system achieves a comparable recognition accuracy to traditional electronic computation in all blind tests.

针对传统基于图像分割和特征提取的手势识别算法在复杂背景下识别准确率低、灵活性差的问题，基于目标检测神经网络的手势识别算法可以有效提高复杂环境下手势识别的准确性。受嵌入式处理器体积和功耗的限制，常用的目标检测神经网络在嵌入式上的识别速度较低，不能满足实时手势识别的要求。在SSD目标检测的基础上对其进行优化，使用MobileNetv3网络实现特征提取，目标检测方面则是使用SSD-lite结构，其使用深度可分离卷积替代普通卷积，实现了轻量化MobileNetv3-SSDLite手势识别算法的设计。针对手势识别的要求，制作了包含不同手势的数据集，利用它在服务器上完成了模型的训练。为了满足嵌入式的算力限制，通过模型的量化压缩将float64的网络参数量化为int8，并压缩网络结构，提高网络在嵌入式上的推理速度，实现基于嵌入式的手势识别。实验结果表明，基于嵌入式的MobileNetv3-SSDLite手势识别算法可以达到平均准确率99.61%，且识别速度达到每秒50帧以上，满足实时手势识别的要求。