Special Session – Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications Henkel, Jörg Siddhu, Lokesh Bauer, Lars Teich, Jürgen Wildermann, Stefan Tahoori, Mehdi Mayahinia, Mahta Castrillon, Jeronimo Khan, Asif Ali Farzaneh, Hamid de Lima, João Paulo C. Chen, Jian-Jia Hakert, Christian Chen, Kuan-Hsun Yang, Chia-Lin Cheng, Hsiang-Yun This paper explores the challenges and opportunities of integrating non-volatile memories (NVMs) into embedded systems for machine learning. NVMs offer advantages such as increased memory density, lower power consumption, non-volatility, and compute-in- memory capabilities. The paper focuses on integrating NVMs into embedded systems, particularly in intermittent computing, where systems operate during periods of available energy. NVM technologies bring persistence closer to the CPU core, enabling efficient designs for energy-constrained scenarios. Next, computation in resistive NVMs is explored, highlighting its potential for accelerating machine learning algorithms. However, challenges related to reliability and device non-idealities need to be addressed. The paper also discusses memory-centric machine learning, leveraging NVMs to overcome the memory wall challenge. By optimizing memory layouts and utilizing probabilistic decision tree execution and neural network sparsity, NVM-based systems can improve cache behavior and reduce unnecessary computations. In conclusion, the paper emphasizes the need for further research and optimization for the widespread adoption of NVMs in embedded systems presenting relevant challenges, especially for machine learning applications. 2023 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/67472 Henkel J, Siddhu L, Bauer L, et al. Special Session – Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications. In: <i>Proceedings of the 2023 International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES)</i>. CASES ’23 Companion. ; 2023:11–20. doi:<a href="https://doi.org/10.1145/3607889.3609088">10.1145/3607889.3609088</a> info:eu-repo/semantics/altIdentifier/doi/10.1145/3607889.3609088 info:eu-repo/semantics/altIdentifier/issn/2643-1726 info:eu-repo/semantics/altIdentifier/isbn/9798400702907 info:eu-repo/semantics/closedAccess