The analysts all agree—the spend for AI hardware (and software) will be going through the roof in short order.

XaLogics’s AI Accelerator with K210 SoC comes with a dual-core RISC-V AI processor featuring low power consumption than its competing Coral USB Accelerator, and Intel Neural Compute Stick 2.

In theory, a heterogeneous multicore device can equip a compute block optimized for any type of operation a given use case can throw at it. A GPU for video processing, a neural network processor for object recognition, a CPU to run the OS, and so on. The different fit-for-purpose cores provide an SoC with more flexibility, and therefore greater performance and lower power consumption across a wider range of workloads, than a homogeneous processor of the same class. 

Nvidia added the TX2 NX to the Jetson product family.

2021 Embedded Processor Report: With the dependable performance-per-watt gains of transistor scaling drawing to a close, how will future generations of processors access the compute necessary to efficiently execute demanding workloads? The answer my come via parallel processing on heterogeneous SoCs.

For autonomous vehicles to progress to their next evolutionary phase, it is essential that vehicles have a more complete understanding of the environment in which they must safely move through.

As part of the Linux Security Summit Europe last October, I participated in a panel around the question, “Would Abandoning the C Language Really Help?”. C, which is the main language used in the Linux kernel, is notorious for having an endless source of vulnerabilities. Just look at the long list of open bugs automatically reported by the fuzzing robot syzbot that are still waiting for a fix.