activity
20202024
most citedDeep Random Forest with Ferroelectric Analog Content Addressable Memory

11 citations · 18 across the 3 of their papers we have counts for

collaborators

5 papers

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.ET20225 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.ET20222 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.ET202111 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

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…