Energy-efficient and Reliable Inference in Nonvolatile Memory under Extreme Operating Conditions

Salonik Resch, S. Karen Khatamifard, Zamshed I Chowdhury, Masoud Zabihi, Zhengyang Zhao, Husrev Cilasun, Jianping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Beyond-edge devices can operate outside the reach of the power grid and without batteries. Such devices can be deployed in large numbers in regions that are difficult to access. Using machine learning, these devices can solve complex problems and relay valuable information back to a host. Many such devices deployed in low Earth orbit can even be used as nanosatellites. Due to the harsh and unpredictable nature of the environment, these devices must be highly energy-efficient, be capable of operating intermittently over a wide temperature range, and be tolerant of radiation. Here, we propose a non-volatile processing-in-memory architecture that is extremely energy-efficient, supports minimal overhead checkpointing for intermittent computing, can operate in a wide range of temperatures, and has a natural resilience to radiation.

Original languageEnglish (US)
Article number3520130
JournalACM Transactions on Embedded Computing Systems
Issue number5
StatePublished - Dec 9 2022

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© 2022 Association for Computing Machinery.


  • Processing in memory
  • beyond-edge computing


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