Browsing by Author "Narduzzi, Simon"

Now showing items 1-5 of 5

    • Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform 

      Narduzzi, Simon; Türetken, Engin; Thiran, Jean-Philippe; Dunbar, L. Andrea (2022-06)
      Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is ...

    • Benchmarking Neuromorphic Computing for Inference 

      Narduzzi, Simon; Mateu, Loreto; Jokic, Petar; Azarkhish, Erfan; Dunbar, L. Andrea (2022-06)
      In the last decade, there has been significant progress in the IoT domain due to the advances in the accuracy of neural networks and the industrialization of efficient neural network accelerator ASICs. However, intelligent ...

    • Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms 

      Narduzzi, Simon; Favre, Dorvan; Nuria, Pazos; Dunbar, L. Andrea (2022-06)
      The rapid development of embedded technologies in recent decades has led to the advent of dedicated inference platforms for deep learning. However, unlike development libraries for the algorithms, hardware deployment is ...

    • Efficient Neural Vision Systems Based on Convolutional Image Acquisition 

      Pad, Pedram; Narduzzi, Simon; Kündig, Clément; Türetken, Engin; Bigdeli, Siavash A.; Dunbar, L. Andrea (2020-06-14)
      Despite the substantial progress made in deep learning in recent years, advanced approaches remain computationally intensive. The trade-off between accuracy and computation time and energy limits their use in real-time ...

    • Optimizing the Consumption of Spiking Neural Networks with Activity Regularization 

      Narduzzi, Simon; Bigdeli, Siavash. A; Liu, Shih-Chii; Dunbar, L. Andrea (2022-05)
      Reducing energy consumption is a critical point for neural network models running on edge devices. In this regard, reducing the number of multiply-accumulate (MAC) operations of Deep Neural Networks (DNNs) running on edge ...