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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

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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.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

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

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…