Deep Learning and Sensor Fusion Driving Onboard Compute Requirements
The consensus about how to perform advanced sensor fusion based on deep-learning computing in autonomous and driverless vehicles converges around heavy lifting Graphics Processing Unit– (GPU-)based central embedded processing units that support tens of Teraflops (One Trillion Floating Point Operations per Second, or TFLOPs). This computing power allows the running of multiple deep-learning inferencing applications, including machine vision, High Definition (HD) map-based positioning (e.g., Simultaneous Localization and Mapping, or SLAM), and driver or passenger monitoring. But this inflation of onboard computing comes at price—not only in terms of the cost of the compute module but also in terms of the power budget impacting cooling needs and Electronic Vehicle (EV) range.
This is prompting multiple hardware, software, and mapping startups to develop light- or lighter-weight solution...
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