Improving Accuracy and Efficiency in DNNs with Approximate Multipliers: Insights from Information Bottleneck Theory

Salar Shakibhamedan, Nima Amirafshar, Axel Jantsch, Nima TaheriNejad

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DOI: 10.3390/ai7090385
Published: 09
Container: AI
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/ai7090385,
  title = {Improving Accuracy and Efficiency in DNNs with Approximate Multipliers: Insights from Information Bottleneck Theory},
  author = {Salar Shakibhamedan and Nima Amirafshar and Axel Jantsch and Nima TaheriNejad},
  year = {2026},
  journal = {AI},
  doi = {10.3390/ai7090385},
  url = {https://doi.org/10.3390/ai7090385}
}

RIS

TY  - JOUR
TI  - Improving Accuracy and Efficiency in DNNs with Approximate Multipliers: Insights from Information Bottleneck Theory
AU  - Salar Shakibhamedan
AU  - Nima Amirafshar
AU  - Axel Jantsch
AU  - Nima TaheriNejad
PY  - 2026
JO  - AI
DO  - 10.3390/ai7090385
UR  - https://doi.org/10.3390/ai7090385
ER  -

APA

Shakibhamedan, S., Amirafshar, N., Jantsch, A., & TaheriNejad, N. (2026). Improving Accuracy and Efficiency in DNNs with Approximate Multipliers: Insights from Information Bottleneck Theory. AI. https://doi.org/10.3390/ai7090385

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doaj · retrieved 2026-11-10T08:28:40.717Z