
FPGA Acceleration of Convolutional Neural Networks (CNNs)
White Paper FPGA Acceleration of Convolutional Neural Networks Overview Convolutional Neural Networks (CNNs) have been shown to be extremely effective at complex image recognition problems.
The S7t-VG6 VectorPath accelerator card offers a 7nm Achronix FPGA that is optimized for high-speed networking and fast, high-capacity memory access. Featuring a QSFP-DD (double-density) cage, the board supports up to 1x 400GbE or 4x 100GbE using the 56G PAM4-enabled Speedster®7t device. An additional QSFP port supports 2x 100GbE, and a 4x MCIO connector for expansion. Sixteen channels of GDDR6 graphics DRAM handle high-bandwidth memory requirements, providing up to 448GB/s.
The FPGA offers large logic and memory resources—up to 692K 6-input lookup tables (LUTs), and 189 Mb embedded RAM. It also provides 2,560 MLPs (machine-learning blocks).
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White Paper FPGA Acceleration of Convolutional Neural Networks Overview Convolutional Neural Networks (CNNs) have been shown to be extremely effective at complex image recognition problems.
PCIe FPGA Card XUP-PL4 UltraScale+ FPGA Low-Profile PCIe Card Dual QSFP28s and DDR4 Need a Price Quote? Jump to Pricing Form Ready to Buy? Check
Efficient Sharing of FPGA Resources in oneAPI Building a Butterfly Crossbar Switch to Solve Resource Sharing in FPGAs The Shared Resource Problem FPGA cards usually
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