Low-power accurate face recognition with NeuroMem IP on FPGA

Low-power accurate face recognition with NeuroMem IP on FPGA

The Advanced Numerical Research and Analysis Group of the Defense R&D Organization of India  demonstrates that the NeuroMem neurons outperform other hardware solutions to recognize faces. Their research project and conclusions are supported by two white papers and a board development featuring the NeuroMem IP installed on a Xilinx FPGA.

Part1 –  Prototyping with NeuroMem CM1K chip

Part2 –  Implementation with NeuroMem IP for FPGA

Glass surface inspection with minimum investment

Glass surface inspection with minimum investment

Surface inspection systems do not have to be a big investment in term of budget, resources and time.

General Vision has developed an AI camera powered by a NeuroMem network to detect defects and which can be assembled in-line with other identical cameras to cover any width of material passing on a belt or float. The cameras can be snapped on a simple din rail and spaced regularly, or not, to monitor 24/7 the quality of glass, plastic, vinyl, wood, paper and pulp, fabrics, printing, and more.

Cameras trained for glass defect detection

Depending on the material and installation, the training can be as simple as training on only one camera and then exporting the knowledge built by its neurons, to the neurons of the other cameras. Sometimes, training may require some tuning for the 2 cameras at each end of the line and this is where the real-time learning capabilities of the neurons is very practical. Read more>>