eNVM

Emerging Embedded Non-Volatile Memory (eNVM) Technologies

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In the face of the memory wall, DRAM technology scaling issues, and ever-increasing application data demands across computing systems from the edge to the cloud, the exploration and evaluation of emerging, alternative memory technologies is an essential pursuit for enabling efficient future memory systems.  We investigate, model, and evaluate the system-level implications of memory technology proposals in varying stages of development, including RRAM, STT-RAM, PCRAM, and Ferroelectric RAM.  The unique physical properties of alternative, embeddable memory technologies necessitate careful co-design among memory cell characteristics, architecture design choices, and application-level optimizations.  Thus, we develop and adopt a full-stack approach to modeling and evaluating systems that leverage eNVM technologies, including application-level fault tolerance analysis and analytical performance, power, and area evaluation frameworks.  

Additionally, we have successfully fabricated and measured the multi-level-cell programming capabilities of Charge Trap Transistors (CTTs) as an incredibly dense and entirely CMOS-compatible eNVM.  Our ongoing work investigates both the design, integration, and fabrication of SoCs leveraging eNVMs and the development of comprehensive tools to facilitate design space exploration and determine the viability of eNVM proposals in different system and application contexts.

Select Publications

2022

L. Pentecost, A. Hankin, M. Donato, M. Hempstead, G.-Y. Wei, and D. Brooks. 2022. “NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories”. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul, South Korea
L. Pentecost, A. Hankin, M. Donato, M. Hempstead, G.-Y. Wei, and D. Brooks. 2022. “NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories”. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul, South Korea

2021

M. M. Sharifi, L. Pentecost, R. Rajaei, A. Kazemi, Q. Lou, G.-Y. Wei, D. Brooks, K. Ni, X. S. Hu, M. Niemier, and M. Donato. 2021. “Application-Driven Design Exploration for Dense Ferroelectric Embedded Non-Volatile Memories
M. M. Sharifi, L. Pentecost, R. Rajaei, A. Kazemi, Q. Lou, G.-Y. Wei, D. Brooks, K. Ni, X. S. Hu, M. Niemier, and M. Donato. 2021. “Application-Driven Design Exploration for Dense Ferroelectric Embedded Non-Volatile Memories

2019

Lillian Pentecost, Marco Donato, Brandon Reagen, Udit Gupta, Siming Ma, Gu Wei, and David Brooks. 2019. “MaxNVM: Maximizing DNN Storage Density and Inference Efficiency With Sparse Encoding and Error Mitigation”. In MICRO ’52: Proceedings of the 52nd Annual IEEE ACM International Symposium on Microarchitecture, Pp. 769–781
Lillian Pentecost, Marco Donato, Brandon Reagen, Udit Gupta, Siming Ma, Gu Wei, and David Brooks. 2019. “MaxNVM: Maximizing DNN Storage Density and Inference Efficiency With Sparse Encoding and Error Mitigation”. In MICRO ’52: Proceedings of the 52nd Annual IEEE ACM International Symposium on Microarchitecture, Pp. 769–781
Marco Donato, Lillian Pentecost, David Brooks, and Gu Wei. 2019. “MEMTI: Optimizing On-Chip Nonvolatile Storage for Visual Multitask Inference at the Edge”. IEEE MICRO, 39, 6
Marco Donato, Lillian Pentecost, David Brooks, and Gu Wei. 2019. “MEMTI: Optimizing On-Chip Nonvolatile Storage for Visual Multitask Inference at the Edge”. IEEE MICRO, 39, 6

2018

Marco Donato, Brandon Reagen, Lillian Pentecost, Udit Gupta, David Brooks, and Gu Wei. 2018. “On-Chip Deep Neural Network Storage With Multi-Level ENVM”. In Design Automation Conference (DAC)
Marco Donato, Brandon Reagen, Lillian Pentecost, Udit Gupta, David Brooks, and Gu Wei. 2018. “On-Chip Deep Neural Network Storage With Multi-Level ENVM”. In Design Automation Conference (DAC)