Xie Chenchen, Li Yuqi, Yan Longhao, Song Sannian, Chen Houpeng, Qi Ruijuan, Li Xi, Zhu Yihang, Yu Lianfeng, Yan Bonan, Tao Yaoyu, Feng Gaoming, Yang Yuchao, Song Zhitang
State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Adv Sci (Weinh). 2025 Sep;12(36):e05678. doi: 10.1002/advs.202505678. Epub 2025 Jul 17.
Object detection, as a fundamental task in computer vision, mainly performs the classification and localization of objects in images or videos. However, traditional edge computing platforms fall short of meeting the demands for state-of-the-art object detection model size and computing power. Here, a 128 Mb phase change memory chip is fabricated with a high memory yield of 99.99999% in a 40 nm node and utilized for efficient in-memory vector-matrix multiplication and in-memory max computation. In particular, in order to mitigate the significant programming energy overheads for large-scale memristor arrays and the reliance on high-precision analog-to-digital-converter (ADC) in compute-in-memory operations, a novel mixed-precision weight mapping strategy is adopted. Compared with traditional schemes, the ADC modules achieve up to a 22.3× reduction in energy consumption while maintaining equivalent network performance. Ultimately, this memristive in-memory object detection system demonstrates 4,180× higher energy efficiency and 228× greater computational throughput compared to GPU implementations.
目标检测作为计算机视觉中的一项基础任务，主要用于对图像或视频中的物体进行分类和定位。然而，传统的边缘计算平台无法满足对先进目标检测模型大小和计算能力的需求。在此，制造了一款128 Mb的相变存储器芯片，其在40纳米节点下具有99.99999%的高存储良率，并用于高效的内存中向量 - 矩阵乘法和内存中最大值计算。特别是，为了减轻大规模忆阻器阵列的显著编程能量开销以及内存计算操作中对高精度模数转换器（ADC）的依赖，采用了一种新颖的混合精度权重映射策略。与传统方案相比，ADC模块在保持等效网络性能的同时，能耗降低了22.3倍。最终，与GPU实现相比，这个忆阻内存目标检测系统展示出高4180倍的能源效率和高228倍的计算吞吐量。
