Intel has quietly launched a mysterious new AI processor that promises to bring inference and deep to the edge – but you won’t be able to plug them into a motherboard anytime soon

Intel has quietly launched a mysterious new AI processor that promises to bring inference and deep to the edge – but you won’t be able to plug them into a motherboard anytime soon
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Intel has launched a new series of AI processors for the edge, promising industrial-grade deep learning inference. The new Atom x7000RE “Amston Lake” chips offer up to twice the number of cores and twice the graphics base clock than the previous x6000RE series, all neatly packaged in a 6W-12W BGA package.

The x7000RE series delivers more performance in a smaller footprint. Featuring up to eight E-cores, it supports LPDDR5/DDR5/DDR4 memory and up to nine PCIe 3.0 lanes, providing robust multitasking capabilities.

Intel says its new processors are designed to withstand harsh conditions, extreme temperature variations, shock and vibration, and to operate in hard-to-reach places. They offer 2 SATA Gen 3.2 ports, up to 4 USB 3.2 Gen 2 ports, a USB Type-C port, 2.5 GbE Ethernet, as well as Intel Wi-Fi, Bluetooth, and 5G platform capabilities.

Embedded, industrial and communication

The x7000RE series consists of four SKUs, all suitable for embedded, industrial and communications use in wide temperature conditions. The x7211RE and x7213RE have 2 cores and relatively lower base frequencies, while the x7433RE has 4 cores and the x7835RE has 8 cores with higher base frequencies.

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All four SKUs support a GPU execution unit count of 16 or 32, as well as Intel Time Cooperative and Time-Sensitive Networking GbE features. The x7000RE offers integrated Intel UHD graphics, Intel DL Boost, Intel AVX2 with INT8 support, and OpenVINO toolkit support.

Intel says the chips will allow customers to easily deploy industry-leading deep learning inference and in smart cities, and “enhance computer vision solutions with integrated AI capabilities and ecosystem-compatible camera modules,” as well as “capturing power and cost.” -efficient performance to enable latency-constrained workloads in robotics and automation.

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