How do we transition from our historical lack of understanding about what’s going on under the hood of complex neural networks, and into an era of AI explainability around how these models operate?

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.