Author

Nima Amirafshar

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Open access Sep 2026

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

Approximate multipliers have potential to improve energy efficiency in Deep Neural Networks but introduce computational errors that degrade accuracy. This paper introduces a novel method, leveraging approximate multipliers to enhance accuracy, while improving computational and energy efficiency. We propose a layer-wise...

Salar Shakibhamedan, Nima Amirafshar, Axel Jantsch et al. · Applied Informatics · 0 citations

#machine learning Preprint Sep 2026

FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining

Approximate multipliers can reduce hardware area and energy consumption in Deep Neural Network (DNN) inference; however, they introduce computational errors. Assessing the accuracy of numerous approximate multiplier designs across diverse DNN models and large-scale datasets remains challenging due to prohibitive evalua...

Rappy Saha, Nima Amirafshar, Jude Haris et al. · 0 citations