Zhang Wenbin, Yao Peng, Gao Bin, Liu Qi, Wu Dong, Zhang Qingtian, Li Yuankun, Qin Qi, Li Jiaming, Zhu Zhenhua, Cai Yi, Wu Dabin, Tang Jianshi, Qian He, Wang Yu, Wu Huaqiang
School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
Science. 2023 Sep 15;381(6663):1205-1211. doi: 10.1126/science.ade3483. Epub 2023 Sep 14.
Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.
对于边缘智能设备而言，学习对于适应不同应用场景和用户极为重要。当前用于训练神经网络的技术需要在计算单元和存储单元之间移动大量数据，这阻碍了在边缘设备上实现学习。我们开发了一种具有改进学习能力和低能耗的完全集成忆阻器芯片。STELLAR架构中的方案，包括其学习算法、硬件实现和平行电导调谐方案，是通过使用忆阻器交叉阵列促进片上学习的通用方法，而与忆阻器器件的类型无关。本研究中执行的任务包括运动控制、图像分类和语音识别。
