Optimizing Deep Learning Models For Raspberry Pi

Deep learning models have become increasingly popular for a wide range of applications, including computer vision, natural language processing, and speech recognition. However, these models typically require large amounts of computational resources, making them challenging to run on low-power devices such as the Raspberry Pi. One approach to addressing this challenge is to use pruning techniques to reduce the size of the deep learning models. Pruning involves removing unimportant weights and connections from the model, resulting in a smaller and more efficient model. Pruning can be done during training or after the model has been trained. Another approach is to optimize the deep learning models specifically for the Raspberry Pi architecture. This can include optimizing the model's architecture and parameters to take advantage of the Raspberry Pi's hardware capabilities, such as its CPU and GPU. Additionally, the model can be optimized for energy efficiency by minimizing the amount of computation required. Pruning and optimizing deep learning models for the Raspberry Pi can help overcome the computational and energy constraints of low-power devices, making it possible to run deep learning models on a wider range of devices. In the following sections, we will explore these approaches in more detail and discuss their effectiveness for optimizing deep learning models for the Raspberry Pi.

Methods for Pruning Deep Neural Networks

This paper presents a survey of methods for pruning deep neural networks, from algorithms first proposed for fully connected networks in the 1990s to the recent methods developed for reducing the size of convolutional neural networks. The paper begins by bringing together many different algorithms by categorising them based on the underlying approach used. It then focuses on three categories: methods that use magnitude-based pruning, methods that utilise clustering to identify redundancy, and methods that utilise sensitivity analysis. Some of the key influencing studies within these categories are presented to illuminate the underlying approaches and results achieved. Most studies on pruning present results from empirical evaluations, which are distributed in the literature as new architectures, algorithms and data sets have evolved with time. This paper brings together the reported results from some key papers in one place by providing a resource that can be used to quickly compare reported results, and trace studies where specific methods, data sets and architectures have been used.