Isaac Sánchez Leal presenterade sitt arbete på sitt halvtidsseminarium
Den 14 mars presenterade doktoranden Isaac Sánchez sitt arbete på ett halvtidsseminarium med titeln " Embedded IoT Implementation of Convolutional Neural Networks using Intelligence Partitioning " Nu fortsätter Isaac sitt arbete med doktorsavhandlingen.
He discussed the challenges faced by computer vision systems in IoT sensor nodes due to main node constrains, energy, latency, and memory. Previous works have attempted to address this issue by reducing model size or distributing processing load to other nodes. However, the impact of these approaches on energy consumption and system latency remains unclear.
His research seeks to contribute to this understanding by exploring the impact of partitioning a Convolutional Neural Network (CNN) between an IoT node and an edge device, with a focus on the partition point, quantization method, and communication technology. Specifically, he identifies possible partitioning points and apply quantization and compression techniques to reduce the data sent between the two partitions. Results show that a 99.8% reduction in transmitted data while keeping a high accuracy of the CNN model. Therefore, the quantization of data facilitates CNN network partitioning with significant reductions in overall latency and node energy consumption.
As a future work, he’d like to deepen in data reduction and pruning methods to find a new CNN partitioning method that facilitates the implementation of CNNs in IoT devices.
