activity
20232025
most citedC-Nash: A Novel Ferroelectric Computing-in-Memory Architecture for Solving Mixed Strategy Nash Equilibrium

6 citations · 22 across the 12 of their papers we have counts for

collaborators

12 papers

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

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.LG2024★ 6 cited

FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing

Chao Li, Zhicheng Xu, Bo Wen +5

In scenarios with limited training data or where explainability is crucial, conventional neural network-based machine learning models often face challenges. In contrast, Bayesian i…

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

Energy-Efficient Cryogenic Ternary Content Addressable Memory using Ferroelectric SQUID

Shamiul Alam, Simon Thomann, Shivendra Singh Parihar +4

Ternary content addressable memories (TCAMs) are useful for certain computing tasks since they allow us to compare a search query with a whole dataset stored in the memory array. T…

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★ 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.AR2023★ 4 cited

SEE-MCAM: Scalable Multi-bit FeFET Content Addressable Memories for Energy Efficient Associative Search

Shengxi Shou, Che-Kai Liu, Sanggeon Yun +7

In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi…