In this article, we explore the use of Edge TPU (Tensor Processing Unit) for real-time point cloud processing. We delve into the concept of autoencoders and how they can be used to classify point clouds in a novel way. By leveraging the power of Edge TPU, we aim to deploy these models at the edge for efficient processing of incoming point cloud data.
Autoencoders
Autoencoders are neural networks that compress data into a lower-dimensional representation, called the bottleneck or latent representation, and then reconstruct the original data from this representation. In the context of point clouds, autoencoders can be used to learn a compact and informative representation of the data. By minimizing the average reconstruction error between the input points and their reconstructed counterparts, we can train the autoencoder to extract the most relevant features from the data.
KeraTuner
To optimize the architecture of the autoencoder, we utilize KeraTuner, a tool for neural architecture search. KeraTuner helps us determine the optimal number of neurons in each layer, excluding the bottleneck and output layers, by performing a grid search ranging from 16 to 128 neurons per layer. This allows us to identify the most effective configuration of the autoencoder’s architecture for point cloud classification.
Optimal Autoencoder
The resulting optimal autoencoder has 26,384 parameters and a total of six layers, excluding the bottleneck and output layers. Each layer has a specific number of neurons determined by KeraTuner’s grid search. We use dropout layers after each fully connected layer, except for the bottleneck and output layers, to improve model performance and generalization.
Deployment at the Edge
Our goal is to deploy this optimized autoencoder at the edge for real-time processing of incoming point cloud data. By utilizing Edge TPU, we can efficiently process large amounts of data on mobile devices or embedded systems. This enables us to classify point clouds in a timely manner, without the need for expensive and bulky hardware.
Conclusion
In summary, this article explores the use of Edge TPU for real-time point cloud processing by leveraging autoencoders and KeraTuner for neural architecture search. By optimizing the architecture of the autoencoder, we can deploy a novel classification model that can efficiently process point clouds at the edge. This approach has numerous applications in various fields such as robotics, computer vision, and IoT.
