activity
20212025
most citedDeep Random Forest with Ferroelectric Analog Content Addressable Memory

11 citations · 20 across the 6 of their papers we have counts for

collaborators

6 papers

cs.ET2025

Ferroelectric FET-based Logic-in-Memory Encoder for Hyperdimensional Computing

Arka Chakraborty, Franz Müller, Thomas Kämpfe +1

Hyperdimensional (HD) computing involves encoding of baseline information into large hypervectors and repeated Boolean operations to generate the output class hypervectors which ar…

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.CR2025

In-Situ Encryption of Single-Transistor Nonvolatile Memories without Density Loss

Sanwar Ahmed Ovy, Jiahui Duan, Md Ashraful Islam Romel +4

Non-volatile memories (NVMs) offer negligible leakage power consumption, high integration density, and data retention, but their non-volatility also raises the risk of data exposur…

physics.app-ph2025★ 4 cited

Engineering-Oriented Design of Drift-Resilient MTJ Random Number Generator via Hybrid Control Strategies

Ran Zhang, Caihua Wan, Yingqian Xu +15

Magnetic Tunnel Junctions (MTJs) have shown great promise as hardware sources for true random number generation (TRNG) due to their intrinsic stochastic switching behavior. However…

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.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…