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Subtractor-Based CNN Inference Accelerator
This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors.
Deep Learning Training with Simulated Approximate Multipliers
The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy.
A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot
A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper.
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