activity
20162026
most citedFeCAM: A Universal Compact Digital and Analog Content Addressable Memory Using Ferroelectric

133 citations · 263 across the 40 of their papers we have counts for

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Showing cs.ETShow all

17 papers · 1 filter

cs.ET2025

BEOL Ferroelectric Compute-in-Memory Ising Machine for Simulated Bifurcation

Yu Qian, Alptekin Vardar, Konrad Seidel +7

Computationally hard combinatorial optimization problems are pervasive in science and engineering, yet their NP-hard nature renders them increasingly inefficient to solve on conven…

cs.ET2025★ 2 cited

Device-Algorithm Co-Design of Ferroelectric Compute-in-Memory In-Situ Annealer for Combinatorial Optimization Problems

Yu Qian, Xianmin Huang, Ranran Wang +5

Combinatorial optimization problems (COPs) are crucial in many applications but are computationally demanding. Traditional Ising annealers address COPs by directly converting them…

cs.ET2025

TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory

Chenyu Ni, Sijie Chen, Che-Kai Liu +8

Pattern search is crucial in numerous analytic applications for retrieving data entries akin to the query. Content Addressable Memories (CAMs), an in-memory computing fabric, direc…

cs.ET2025★ 3 cited

TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFET-1T Compute-in-Memory Design

Yifei Zhou, Thomas Kämpfe, Kai Ni +3

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the "…

cs.ET2024

Energy Efficient Dual Designs of FeFET-Based Analog In-Memory Computing with Inherent Shift-Add Capability

Zeyu Yang, Qingrong Huang, Yu Qian +3

In-memory computing (IMC) architecture emerges as a promising paradigm, improving the energy efficiency of multiply-and-accumulate (MAC) operations within DNNs by integrating the p…

cs.ET2024★ 1 cited

A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI

Xunzhao Yin, Hamza Errahmouni Barkam, Franz Müller +14

Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Comprising…

cs.ET2024★ 1 cited

HyCiM: A Hybrid Computing-in-Memory QUBO Solver for General Combinatorial Optimization Problems with Inequality Constraints

Yu Qian, Zeyu Yang, Kai Ni +3

Computationally challenging combinatorial optimization problems (COPs) play a fundamental role in various applications. To tackle COPs, many Ising machines and Quadratic Unconstrai…

cs.ET2024★ 6 cited

C-Nash: A Novel Ferroelectric Computing-in-Memory Architecture for Solving Mixed Strategy Nash Equilibrium

Yu Qian, Kai Ni, Thomas Kämpfe +2

The concept of Nash equilibrium (NE), pivotal within game theory, has garnered widespread attention across numerous industries. Recent advancements introduced several quantum Nash…

cs.ET2024★ 2 cited

FeReX: A Reconfigurable Design of Multi-bit Ferroelectric Compute-in-Memory for Nearest Neighbor Search

Zhicheng Xu, Che-Kai Liu, Chao Li +7

Rapid advancements in artificial intelligence have given rise to transformative models, profoundly impacting our lives. These models demand massive volumes of data to operate effec…

cs.ET2024★ 1 cited

Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET

Yifei Zhou, Xuchu Huang, Jianyi Yang +4

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as 'memory wall' issue.…

cs.ET2023

Reconfigurable Frequency Multipliers Based on Complementary Ferroelectric Transistors

Haotian Xu, Jianyi Yang, Cheng Zhuo +3

Frequency multipliers, a class of essential electronic components, play a pivotal role in contemporary signal processing and communication systems. They serve as crucial building b…

cs.ET2023★ 44 cited

A Ferroelectric Compute-in-Memory Annealer for Combinatorial Optimization Problems

Xunzhao Yin, Yu Qian, Alptekin Vardar +11

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications, including logistical planning, resource allocation, chip design, drug explorati…

cs.ET2022★ 5 cited

A Homogeneous Processing Fabric for Matrix-Vector Multiplication and Associative Search Using Ferroelectric Time-Domain Compute-in-Memory

Xunzhao Yin, Qingrong Huang, Franz Müller +8

In this work, we propose a ferroelectric FET(FeFET) time-domain compute-in-memory (TD-CiM) array as a homogeneous processing fabric for binary multiplication-accumulation (MAC) and…

cs.ET2022★ 2 cited

An Ultra-Compact Single FeFET Binary and Multi-Bit Associative Search Engine

Xunzhao Yin, Franz Müller, Qingrong Huang +12

Content addressable memory (CAM) is widely used in associative search tasks for its highly parallel pattern matching capability. To accommodate the increasingly complex and data-in…

cs.ET2021★ 11 cited

Deep Random Forest with Ferroelectric Analog Content Addressable Memory

Xunzhao Yin, Franz Müller, Ann Franchesca Laguna +14

Deep random forest (DRF), which incorporates the core features of deep learning and random forest (RF), exhibits comparable classification accuracy, interpretability, and low memor…

cs.ET2020★ 133 cited

FeCAM: A Universal Compact Digital and Analog Content Addressable Memory Using Ferroelectric

Xunzhao Yin, Chao Li, Qingrong Huang +5

Ferroelectric field effect transistors (FeFETs) are being actively investigated with the potential for in-memory computing (IMC) over other non-volatile memories (NVMs). Content Ad…

cs.ET2016★ 3 cited

Efficient Analog Circuits for Boolean Satisfiability

Xunzhao Yin, Behnam Sedighi, Melinda Varga +3

Efficient solutions to NP-complete problems would significantly benefit both science and industry. However, such problems are intractable on digital computers based on the von Neum…