Machine Learning on Advanced RF Systems

Direct ML inferences on advanced RF system hardware is extremely challenging but successes allow disruptive advantage in ultra-modern RF operating spaces. Current systems could be improved near-term, but to achieve full advantage of real-time RFML processing, new RF system architectures are required. This presentation will cover challenges/solutions for low level RFML along with fundamental architecture limitations and concepts for updated architecture optimizing RFML performance.