Why Better Mapping Technology Is Critical To Autonomous Vehicles

By John Koon - 01 Dec, 2022 - Comments: 0

Autonomous cars find the way to their destination using a number of critical technologies, including some version of a global position system and a central brain to interpret that and other data. But many of those technologies are not reliable or accurate enough today, and may not be for years to come. There are numerous reports of vehicles missing their stop, or trucks being guided into all... » read more

Week In Review: Auto, Security, Pervasive Computing

By Susan Rambo - 13 Mar, 2020 - Comments: 0

AI, machine learning Cadence says it has optimized its Tensilica HiFi digital signal processor IP to efficiently execute TensorFlow Lite for Microcontrollers, which are used in Google’s machine learning platform for edge. This means developers of AI/ML on the edge systems can now put better audio processing on edge devices with ML applications like keyword detection, audio scene detection, n... » read more

SLX Multi-Objective Optimization (MOPT)

By Silexica - 24 Apr, 2019 - Comments: 0

Technologies such as autonomous cars and 5G communication are seeing a rapid increase in the number of processing elements (PE) per platform. Where software professionals were used to programming one, two or a handful of cores, the game has now changed. Intel’s Many Integrated Core Architecture [3] contains up to 78 cores, Nvidia Tegra XI[2] has up to 260 cores and Adapteva’s Epiphany-V[1] ... » read more

LiDAR Goes Back To The Future

By Jeff Dorsch - 12 Mar, 2018 - Comments: 0

LiDAR is emerging as an increasingly important piece of the enabling technology in autonomous driving, along with advanced computer vision and radar sensor chips. But LiDAR systems also are finding their way into a variety of other applications, such as industrial automation, including robotics, and unmanned aerial vehicles. Advanced mapping is another rapidly growing market for LiDAR, which... » read more

Bridging Machine Learning’s Divide

By Brian Bailey - 08 Feb, 2018 - Comments: 0

There is a growing divide between those researching [getkc id="305" comment="machine learning"] (ML) in the cloud and those trying to perform inferencing using limited resources and power budgets. Researchers are using the most cost-effective hardware available to them, which happens to be GPUs filled with floating point arithmetic units. But this is an untenable solution for embedded infere... » read more

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tag: mapping

Why Better Mapping Technology Is Critical To Autonomous Vehicles

Week In Review: Auto, Security, Pervasive Computing

SLX Multi-Objective Optimization (MOPT)

LiDAR Goes Back To The Future

Bridging Machine Learning’s Divide

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