Deep learning Framework
Easics' deep learning framework can support different neural networks based on existing frameworks like Tensorflow, Caffé, Keras, python, C/C++, … The input for the framework is the network description and the weights of the trained deep learning model. The network description is converted (if necessary) to C++ and used to build the sequencer. The weights of the trained model are converted (floating point quantization) in an fixed point image. Via an API the sequencer and the image are uploaded and stored on the SDRAM connected to the FPGA.
The classification result (what & where it is) of the deep learning algorithm will be sent to the application where the detection of the result will be applied. We can supply a complete system design around the Deep learning core including camera interfaces or external interfaces. A standard solution can combine our TCP Offload Engine with the deep learning core.
Deep learning on FPGA
Benefits of FPGA technology
Using FPGA technologies has the following advantages:
High performance per Watt and low latency make it suitable for real-time embedded applications.
The FPGA logic can be shaped to match any network architecture.
Performance, cost and power will define the FPGA of choice.
Future proof and scalable solution as the FPGA architecture can be re-configured for future neural networks or only update the weights.
Easics' framework offers a flexible approach and a fast-time to market.
The deep learning core can be easily integrated with other CPU’s, vision functionality and connectivity.
We can provide you an ARIA 10 SoM development kit. The input and output interface is ethernet. We can provide this with the DNN of your choice e.g. Yolo V2 or V3, Resnet, mobilenet, tinyyolo, ...