A 20Mb Embedded STT-MRAM Array Achieving 72% Write Energy Reduction with Self-termination Write Schemes in 16nm FinFET Logic Process

Ito, Takashi; Saito, Tomoya; Taito, Yasuhiko; Sonoda, Keiichiro; Watanabe, G.; Matsubara, Ken; Kanda, Akihiko; Shimoi, Takahiro; Takeda, Kouichi; Kono, Takashi

doi:10.1109/iedm19574.2021.9720523

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15.8 A 22nm 10.8Mb Embedded STT-MRAM Macro Achieving over 200MHz Random-Read Access and a 10.4MB/s Write Throughput with an In-Field Programmable 0.3Mb MTJ-OTP for High-End MCUs

Ogawa,

Matsubara,

Taito

et al. 2024

2024 IEEE International Solid-State Circuits Conference (ISSCC)

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15.8 A 22nm 10.8Mb Embedded STT-MRAM Macro Achieving over 200MHz Random-Read Access and a 10.4MB/s Write Throughput with an In-Field Programmable 0.3Mb MTJ-OTP for High-End MCUs

Ogawa,

Matsubara,

Taito

et al. 2024

2024 IEEE International Solid-State Circuits Conference (ISSCC)

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In-Memory Computing for Machine Learning and Deep Learning

Lepri

Glukhov

Cattaneo

et al. 2023

IEEE J. Electron Devices Soc.

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In-memory computing (IMC) aims at executing numerical operations via physical processes, such as current summation and charge collection, thus accelerating common computing tasks including the matrix-vector multiplication. While extremely promising for memory-intensive processing such as machine learning and deep learning, the IMC design and realization must face significant challenges due to device and circuit nonidealities. This work provides an overview of the research trends and options for IMC-based implementations of deep learning accelerators with emerging memory technologies. The device technologies, the computing primitives, and the digital/analog/mixed design approaches are presented. Finally, the major device issues and metrics for IMC are discussed and benchmarked.

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A 20Mb Embedded STT-MRAM Array Achieving 72% Write Energy Reduction with Self-termination Write Schemes in 16nm FinFET Logic Process

Cited by 2 publications

References 2 publications

15.8 A 22nm 10.8Mb Embedded STT-MRAM Macro Achieving over 200MHz Random-Read Access and a 10.4MB/s Write Throughput with an In-Field Programmable 0.3Mb MTJ-OTP for High-End MCUs

15.8 A 22nm 10.8Mb Embedded STT-MRAM Macro Achieving over 200MHz Random-Read Access and a 10.4MB/s Write Throughput with an In-Field Programmable 0.3Mb MTJ-OTP for High-End MCUs

In-Memory Computing for Machine Learning and Deep Learning

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