
Intel Reportedly Canceled Thunder Bay Hybrid SoC
Intel has quietly canceled the hybrid Thunder Bay system-on-chip (SoC), which integrates general-purpose CPU cores and laptop vision-focused Movidius {hardware}. The chipmaker does not reveal the explanations behind their determination, nevertheless it appears like Intel’s CPUs and video processing models (VPUs) will stay separate for now.
“Take away Thunder Bay particular code as product is canceled and no finish buyer or consumer”, Linux patch found by Phoronix reader.
Intel has stored the small print of the Thunder Bay SoC non-public. Based mostly on Linux patches uncovered by PhoronixThe Thunder Bay SoC was alleged to be a low-power design, packing Arm Cortex-A53 CPU cores and Movidius VPU {hardware} (which Intel acquired by buying Movidius in 2016). Nonetheless, the precise configuration of the product was unknown.
Intel’s Thunder Bay SoC was designed for industrial and Web of Issues functions that require laptop imaginative and prescient acceleration and basic goal processing capabilities. Such edge computing functions are anticipated to change into more and more frequent in sensible cities.
In the meantime, customers of functions that want CPUs and VPUs appear maybe glad with edge servers working Xeon and Movidius silicon, just like the Keem Bay accelerator card launched in 2019.
Additionally, as machine studying acceleration turns into ubiquitous, many functions might undertake completely different {hardware}, together with Intel’s personal Habana Gaudi, Nvidia’s GPUs or Jetson SoCs (with built-in GPU cores). In consequence, it’s not but clear whether or not Intel will determine to introduce a Thunder Bay-like SoC sooner or later and the way this potential product can be configured.
Whereas Movidius VPUs aren’t usually talked about, they do have their advantages. The Movidius picture processing unit packs general-purpose MIPS cores with programmable 128-bit vector processing (referred to as SHAVE cores), varied {hardware} accelerators, and picture sign processing capabilities. Due to this fact, VPUs are barely extra tailor-made for edge computing functions than high-performance AI/ML accelerators by way of energy consumption and footprint.
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