cfaed Publications

@InProceedings{henkel_cases23,
author = {J\"{o}rg Henkel and Lokesh Siddhu and Lars Bauer and J\"{u}rgen Teich and Stefan Wildermann and Mehdi Tahoori and Mahta Mayahinia and Jeronimo Castrillon and Asif Ali Khan and Hamid Farzaneh and Jo\~{a}o Paulo C. de Lima and Jian-Jia Chen and Christian Hakert and Kuan-Hsun Chen and Chia-Lin Yang and Hsiang-Yun Cheng},
booktitle = {Proceedings of the 2023 International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES)},
title = {Special Session -- Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications},
location = {Hamburg, Germany},
abstract = {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.},
pages = {11--20},
url = {https://ieeexplore.ieee.org/abstract/document/10316216},
doi = {10.1145/3607889.3609088},
isbn = {9798400702907},
series = {CASES '23 Companion},
issn = {2643-1726},
month = sep,
numpages = {10},
year = {2023},
}