Content Addressable Memories and Transformable Logic Circuits Based on Ferroelectric Reconfigurable Transistors for In-Memory Computing

计算机科学 晶体管 与非门 XNOR门 晶体管计数 电子线路 计算机硬件 逻辑门 冯·诺依曼建筑 电子工程 嵌入式系统 电气工程 电压 工程类 算法 操作系统
作者
Zijing Zhao,Junzhe Kang,Ashwin Tunga,Hojoon Ryu,Ankit Shukla,Shaloo Rakheja,Wenjuan Zhu
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.2307.03660
摘要

As a promising alternative to the Von Neumann architecture, in-memory computing holds the promise of delivering high computing capacity while consuming low power. Content addressable memory (CAM) can implement pattern matching and distance measurement in memory with massive parallelism, making them highly desirable for data-intensive applications. In this paper, we propose and demonstrate a novel 1-transistor-per-bit CAM based on the ferroelectric reconfigurable transistor. By exploiting the switchable polarity of the ferroelectric reconfigurable transistor, XOR/XNOR-like matching operation in CAM can be realized in a single transistor. By eliminating the need for the complementary circuit, these non-volatile CAMs based on reconfigurable transistors can offer a significant improvement in area and energy efficiency compared to conventional CAMs. NAND- and NOR-arrays of CAMs are also demonstrated, which enable multi-bit matching in a single reading operation. In addition, the NOR array of CAM cells effectively measures the Hamming distance between the input query and stored entries. Furthermore, utilizing the switchable polarity of these ferroelectric Schottky barrier transistors, we demonstrate reconfigurable logic gates with NAND/NOR dual functions, whose input-output mapping can be transformed in real-time without changing the layout. These reconfigurable circuits will serve as important building blocks for high-density data-stream processors and reconfigurable Application-Specific Integrated Circuits (r-ASICs). The CAMs and transformable logic gates based on ferroelectric reconfigurable transistors will have broad applications in data-intensive applications from image processing to machine learning and artificial intelligence.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
1秒前
傻傻的语海完成签到,获得积分10
1秒前
1秒前
相俊杰发布了新的文献求助10
2秒前
expuery完成签到,获得积分10
3秒前
悦耳扬发布了新的文献求助10
3秒前
3秒前
DMF完成签到,获得积分10
3秒前
JamesPei应助vvv采纳,获得10
4秒前
4秒前
打打应助CT采纳,获得10
4秒前
aaa发布了新的文献求助10
4秒前
hzhang0807发布了新的文献求助10
5秒前
ZetaGundam发布了新的文献求助10
5秒前
HEYL完成签到,获得积分10
5秒前
在水一方应助qayqay003采纳,获得10
6秒前
6秒前
SciGPT应助彩色的鸡翅采纳,获得10
7秒前
7秒前
DMF发布了新的文献求助10
7秒前
Zlinco完成签到,获得积分10
7秒前
7秒前
踏实采波发布了新的文献求助10
7秒前
8秒前
深情安青应助Z777采纳,获得10
8秒前
8秒前
8秒前
8秒前
细心的完成签到,获得积分20
9秒前
9秒前
atriumz应助丰富的银耳汤采纳,获得10
10秒前
由于发布了新的文献求助10
10秒前
Gauss应助燕子采纳,获得30
10秒前
wzz完成签到,获得积分10
10秒前
健康的绮晴完成签到,获得积分20
10秒前
11秒前
may发布了新的文献求助10
11秒前
阔达寄容发布了新的文献求助10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Organometallic Chemistry of the Transition Metals 800
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6442631
求助须知:如何正确求助?哪些是违规求助? 8256562
关于积分的说明 17582478
捐赠科研通 5501197
什么是DOI,文献DOI怎么找? 2900625
邀请新用户注册赠送积分活动 1877550
关于科研通互助平台的介绍 1717279