---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jörg
      foaf_name: Henkel, Jörg
      foaf_surname: Henkel
  - foaf_Person:
      foaf_givenName: Lokesh
      foaf_name: Siddhu, Lokesh
      foaf_surname: Siddhu
  - foaf_Person:
      foaf_givenName: Lars
      foaf_name: Bauer, Lars
      foaf_surname: Bauer
  - foaf_Person:
      foaf_givenName: Jürgen
      foaf_name: Teich, Jürgen
      foaf_surname: Teich
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Wildermann, Stefan
      foaf_surname: Wildermann
  - foaf_Person:
      foaf_givenName: Mehdi
      foaf_name: Tahoori, Mehdi
      foaf_surname: Tahoori
  - foaf_Person:
      foaf_givenName: Mahta
      foaf_name: Mayahinia, Mahta
      foaf_surname: Mayahinia
  - foaf_Person:
      foaf_givenName: Jeronimo
      foaf_name: Castrillon, Jeronimo
      foaf_surname: Castrillon
  - foaf_Person:
      foaf_givenName: Asif Ali
      foaf_name: Khan, Asif Ali
      foaf_surname: Khan
  - foaf_Person:
      foaf_givenName: Hamid
      foaf_name: Farzaneh, Hamid
      foaf_surname: Farzaneh
  - foaf_Person:
      foaf_givenName: João Paulo C.
      foaf_name: de Lima, João Paulo C.
      foaf_surname: de Lima
  - foaf_Person:
      foaf_givenName: Jian-Jia
      foaf_name: Chen, Jian-Jia
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Hakert, Christian
      foaf_surname: Hakert
  - foaf_Person:
      foaf_givenName: Kuan-Hsun
      foaf_name: Chen, Kuan-Hsun
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Chia-Lin
      foaf_name: Yang, Chia-Lin
      foaf_surname: Yang
  - foaf_Person:
      foaf_givenName: Hsiang-Yun
      foaf_name: Cheng, Hsiang-Yun
      foaf_surname: Cheng
  bibo_doi: 10.1145/3607889.3609088
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2643-1726
  - http://id.crossref.org/issn/9798400702907
  dct_title: 'Special Session – Non-Volatile Memories: Challenges and Opportunities
    for Embedded System Architectures with Focus on Machine Learning Applications@'
...