计算机科学
半导体存储器
内存刷新
数模转换器
模数转换器
计算机存储器
计算机硬件
计算机体系结构
电子工程
电气工程
工程类
电压
作者
Jiseong Im,Jonghyun Ko,Joon Hwang,Jangsaeng Kim,Wonjun Shin,Ryun‐Han Koo,Minkyu Park,Sungho Park,Woo Young Choi,Jae‐Joon Kim,Jong‐Ho Lee
标识
DOI:10.1002/aisy.202400594
摘要
Compute‐in‐memory (CIM) technology based on emerging nonvolatile memories (NVMs) has shown promise in enhancing artificial intelligence applications by integrating computation directly within NVM arrays. However, the efficiency of CIM systems is often curtailed by the substantial overhead that is caused by traditional complementary metal‐oxide‐semiconductor (CMOS)‐based analog‐to‐digital converters (ADCs). Here, we report an in‐memory ADC (IMADC) that leverages NVMs to perform the dual functionalities of reference generation and voltage comparison, effectively minimizing the area occupancy and energy consumption, is reported. The IMADC not only significantly outperforms traditional ADCs but also enables the inherent processing of nonlinear activation functions such as the sigmoid function, which is required for neural networks. The IMADC‐based CIM system achieves software‐comparable accuracy in CIFAR‐10 image classification on the VGG‐9 network. The IMADC exhibits significantly reduced area occupancy (45 μm 2 ) and energy consumption (29.6 fJ) compared to conventional CMOS‐based ADCs. The IMADC, compatible with various types of NVMs, demonstrates significant potential for enhancing the efficiency of CIM systems in terms of area occupancy and energy consumption.
科研通智能强力驱动
Strongly Powered by AbleSci AI