
Comparing FPGA RTL to HLS C/C++ using a Networking Example
White Paper Comparing FPGA RTL to HLS C/C++ using a Networking Example Overview Most FPGA programmers believe that high-level tools always emit larger bitstreams as
BittWare’s 250-U2 is a Computational Storage Processor conforming to the U.2 form factor. It features an AMD Kintex UltraScale+ FPGA directly coupled to local DDR4 memory. This energy-efficient, flexible compute node is intended to be deployed within conventional U.2 NVMe storage arrays (approximately 1:8 ratio) allowing FPGA-accelerated instances of:
The 250-U2 can be wholly programmed by customers developing in-house capabilities or delivered as a ready-to-run pre-configured solution featuring Eideticom’s NoLoad® IP. The 250-U2 is front-serviceable in a 1U chassis and can be mixed in with storage units in the same server, allowing users to mix-and-match storage and acceleration.
The HRG gives you much more detail about the card such as block diagrams, tables and descriptions.
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White Paper Comparing FPGA RTL to HLS C/C++ using a Networking Example Overview Most FPGA programmers believe that high-level tools always emit larger bitstreams as
White Paper FPGA-Accelerated NVMe Storage Solutions Using the BittWare 250 series accelerators Overview In recent years, the migration towards NAND flash-based storage and the introduction
Go Back to IP & Solutions Dynamic Neural Accelerator ML Framework EdgeCortix Dynamic Neural Accelerator (DNA), is a flexible IP core for deep learning inference
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.