Salar Shakibhamedan

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...