Edge Deep Learning
As the hardware capabilities have grown, so has the size of the neural networks. But in recent years there has been a shift in focus to make the networks as small as possible. This means that the power of Deep Learning has become available for mobile phones, IoT devices and other embedded systems.
Synective Labs take part in the edge deep learning research by combining our knowledge of FPGA development with the latest deep learning architectures. We have also worked with deep learning on embedded CPUs such as the Raspberry Pi platform. Edge deep learning requires algorithmic insights as well as the know-how of how to implement, and Synective stands firmly with one foot in each world.
We offer services:
Edge Convolutional Neural Networks
Deep Learning on FPGA
Feasibility Studies and Consultation
Hardware Acceleration of Machine Learning Algorithms
Training Data Collection
Synective has developed a demo system to showcase the power of embedded FPGA Deep Learning. It was able to classify hand-written digits using nothing but a tiny, low-end FPGA and a camera.
Synective has designed a CNN that is optimized with respect to memory footprint and inference speed. The network is implemented and runs on the ARM processor of the Raspberry Pi.
Synective continually take on thesis workers that improve our embedded Deep Learning system.