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​With the elastic AI.creator it is possible to design AI models especially optimised for FPGAs, to train them and to translate them into a hardware description.

First of all, it provides custom optimised layers from which a suitable model is assembled.

The finished model then undergoes its training and is then translated into a VHDL representation of the corresponding FPGA target configuration.

These VHDL files can be compiled using third-party software such as Vivado Xilinx for the Elastic Node or other devices.

The Creator is based on the work in the LUTNet project. The source code and information on how to use it can be found on the associated Github page.

Responsible: Lukas Einhaus

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​The elastic AI.runtime is a continuation of the Elastic IoT Platform and in a sense represents the infrastructure of the ecosystem. It is a platform for controlling, managing and monitoring distributed AI systems. Its structure consists of an embedded runtime, a cloud runtime and the monitor, a cloud application for supervisory purposes.

The embedded runtime is used on the respective end devices of the distributed embedded systems and acts as an interface between the server and the respective devices. It takes care of general control and communication, resource management as well as the selection of FPGA configurations and – by means of the Elastic Node Middleware – their deployment and their operation.

The individual end devices have their counterparts on the server side in the form of Digital Twins. These simultaneously serve as a model representation of the physical network elements, as load balancers and as intermediaries to them. The Digital Twins also provide abstractions that allow greater flexibility in dealing with concrete hardware which is dependent on specific protocols and structures.

Finally, the Monitor is designed to oversee the entire assemblage and, with the help of the Digital Twins, creates an overview of the distributed system and the status of its elements.

Full source code and technical details on Github:

• Server segment of the elastic AI.runtime
• Implementation for the Elastic Node v5
• C implementation of the custom runtime protocol

Responsible: Gregor Schiele

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​The accompanying hardware is the fifth iteration of our own Elastic Node.

It is a small, very energy-efficient endpoint device for distributed sensor networks that optionally connects to cloud or edge AI through its wireless module, or relies on on-device AI.

Since its initial release, the hardware has been steadily refined. The Elastic Node v5 is now less than 5.8 cm high and only 3.4 cm wide, making it suitable for very space-constrained deployments.

Its heterogeneous design essentially combines a power-saving microcontroller (MCU), a cache for data and an embedded FPGA which is primarily intended for AI applications, together with memory for additional AI models.

The microcontroller takes over comparatively simple tasks such as general controlling and reading out of sensor data, while the FPGA takes care of more specific tasks for which suitable neural networks were previously assembled by means of the Creator.

Thanks to its model storage, the Elastic Node can dynamically reconfigure itself in situ and thus always have the appropriate neural network ready for varying requirements.

Other features include a power consumption monitor and the possibility of expanding functionality by adding extension boards.

Responsible: Chao Qian