A crossbar array of magnetoresistive memory devices for in-memory computing

By Seungchul Jung, Hyungwoo Lee, Sungmeen Myung, Hyunsoo Kim, Seung Keun Yoon, Soonwan Kwon, Yongmin Ju, Minje Kim, Wooseok Yi, Shinhee Han and 9 more. Nature, 2022

590 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/88b7dfd23ccefed0abb6979f2a780491148bb102

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Abu Sebastian
2. Wei Dang Lu
3. Bryan Perozzi
4. Chengqi Zhang
5. William L. Hamilton

In Electrical and Electronic Engineering

1. J. Joshua Yang
2. Qiangfei Xia
3. Abu Sebastian
4. M. Le Gallo
5. Zhongrui Wang
6. Wei Dang Lu
7. Hao Jiang
8. Miao Hu

Citations in the corpus

• Memristive technologies for data storage, computation, encryption, and radio-frequency communication (2022, methodology): “Chips targeting DNN inference with IMC usingmemristive devices have been fabricated using RRAM (41, 56), PCM (57, 58), andMRAM (59, 60).”
• A compute-in-memory chip based on resistive random-access memory (2022, unknown): “These techniques can be more generally applied to other non-volatile resistive memory technologies such as phase-change memory 8,17,21,23,24 , magnetoresistive RAM 48 and ferroelectric field-effect transistors 49 .”
• Thousands of conductance levels in memristors integrated on CMOS (2023, methodology): “The extra system cost caused by the denoising process is estimated in Supplementary Information Section 9.”