
FPGA Acceleration of Binary Weighted Neural Network Inference
White Paper FPGA Acceleration of Binary Weighted Neural Network Inference One of the features of YOLOv3 is multiple-object recognition in a single image. We used
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.
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White Paper FPGA Acceleration of Binary Weighted Neural Network Inference One of the features of YOLOv3 is multiple-object recognition in a single image. We used
Intel® oneAPI™ High-level FPGA Development Menu Evaluating oneAPI Accelerator Cards ASPs More Info Contact/Where to Buy Is oneAPI Right for You? You may already know
Introducing the IA-420f: A Powerful Low-Profile FPGA Card Powered by Intel Agilex Tap Into the Power of Agilex The new Intel Agilex FPGAs are more
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