Xilinx recently tapped a startup for its AI accelerating software platform. In what ways might this platform add extra benefits for FPGAs in contrast to GPUs?
It’s no secret that artificial intelligence is rapidly unfolding and making its way into virtually every field. AI imposes a unique workload on its compute platforms, one where parallelization is crucial. For this reason, GPUs, which generally boast over 1000 cores, have become preferable to CPUs when running neural networks, according to Medium contributor Connor Shorten.
By Robert LARA
June 26, 2020
