The Edge of the network is quickly becoming an integral aspect of AI processing’s potential.
Markets are increasingly interested in exploiting AI and machine learning (AI / ML). They are also showing strong demand for low-power AI / ML computing tools for the Edge Network. This is very promising for engineers building careers in this space.
It’s clear that embedded vision applications from Edge need devices with certain design features and performance with minimal power consumption, high effectiveness, reliability, and a small form factor.
In this whitepaper, Lattice shows how you can build such devices using a new FPGA family designed for such applications.
The key takeaways are:
- Identifying the best options of FPGAs for the developers from the comparison of the new product and other FPGAS.
- Acquiring knowledge to build specialized, small-footprint, low-power FPGAs.
- Obtaining more knowledge about how these new FPGAs will meet the needs for video signal aggregation and image co-processing!
- Identifying how CrossLink-NX can be used for sensor aggregation and bridging for video surveillance and security applications.
