NetSpeed Leverages Machine Learning for Automotive IC End-to-End QoS Solutions
A couple of weeks back I wrote an article about the use of machine learning and deep neural networks in self-driving cars. Now I find that machine learning is also being applied to help build advanced end-to-end QoS (quality of service) solutions for the automotive IC market. With the advent of self-driving cars comes requirements to be able to deal with all of the data streams coming into the car. Many automotive system designers are turning to heterogeneous multi-core SoCs (system-on-chip) to meet the requirements of increased performance, reduced power consumption and increased overall system reliability.
These new SoCs are not your typical homogeneous multi-core ICs. Instead, they are heterogeneous SoCs with a variety of different compute engines each with widely varying requirements for QoS. Automotive SoCs may include CPU clusters, GPUs, communications cores (Wi-Fi, Blue-tooth, USB, 4G modem etc.), multimedia cores, GPS, DSPs, cameras, gesture processing, display / video and security modules to name a few.
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