Work

High-Density STT-Assisted SOT-MRAM (SAS-MRAM) for Energy-Efficient AI Applications

Year: 2024

Type: article

Source: IEEE Transactions on Magnetics

DOI: 10.1109/tmag.2024.3486616

Authors: Fen Xue, William Hwang, Fan Zhang, Wilman Tsai, Deliang Fan, Shan X. Wang

Institutions: Stanford University, Johns Hopkins University

Language: English

Topic: Magnetic properties of thin films

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: closed

Funders: National Science Foundation, Semiconductor Research Corporation

Awards: Collaborative Research: FuSe: Efficient Situation-Aware AI Processing in Advanced 2-Terminal SOT-MRAM, NNCI: nano@stanford, G2677204638, ACED Fab: Ultrafast, low-power AI chip with a new class of MRAM for learning and inference at edge, PFI-RP: Resilient and Energy-Efficient Memory Chips for Enhanced Mobile AI and Personalized Machine Learning

OpenAlex ID: W4403761228

FWCI: 0.955

Cites: 33

Cited by: 6

Related to: 10