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

The platform exploits the reconfigurable logic of Field-Programmable Gate Arrays to implement approximate multipliers directly in hardware, eliminating the need for LUT-based emulation on CPU/GPU platforms and thereby enabling efficient DNN inference while significantly reducing evaluation time on large datasets.

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