Justice Department Combats California on Net Neutrality

DOJ Taking On State of California Over Net Neutrality

Daily Tech Briefing Oct. 5
Written By
eWEEK Staff
eWEEK Staff
Oct 5, 2018
2 minute read
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Today’s topics include the Department of Justice combating California on net neutrality, and Xilinx putting FPGAs into accelerator cards for data centers.

The U.S. Department of Justice is about to take on the state of California in a net neutrality battle. At stake is both the definition of net neutrality and the limits of a state’s ability to regulate the internet.

The basis of the questions involved is whether the internet is an instrument of interstate commerce. Section 8 of Article 1 of the Constitution relating to interstate commerce is not open to interpretation, and California is subject to that article of the Constitution. So, if the internet is an instrument of interstate commerce, how will California justify what appears to be a clear violation of the commerce clause of the Constitution when it passed its new net neutrality law?


It’s hard to know what the arguments will be once the case goes to federal court. But the reasons given so far seem to be that California’s new net neutrality law is good for the citizens of that state, and that the FCC took away some level of freedom when it reclassified the internet.

Xilinx is launching a new line of PCIe accelerator cards powered by the company’s UltraScale+ programmable silicon and aimed at such modern workloads as machine learning inference, video processing and data analytics.

Xilinx President and CEO Victor Peng announced the Alveo portfolio Oct. 2 during his keynote address at the company’s second Xilinx Developer Forum, noting that it was the first time Xilinx was offering the acceleration capabilities of its field-programmable gate arrays in a board form factor.

In addition, Xilinx is partnering with Advanced Micro Devices to bring together AMD’s Epyc server CPUs built on the company’s “Zen” microarchitecture and Alveo accelerator cards in a system aimed at machine learning inference workloads.

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