
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
SNIA defines several components collectively called “computational storage.” For a typical IA-220-U2 deployment, the acceleration tasks are called CSS (computational storage services).
For example, the IA-220-U2’s Agilex FPGA can perform compression much faster than a CPU—even surpassing the transfer rate of the storage system for transparent compression.
CSPs (like the IA-220-U2) work alongside FLASH, providing accelerated computational storage services (CSS) by performing compute, such as compression or encryption. This lets users build out storage using standard SSDs instead of being locked into a single vendor’s FLASH storage.
What features make the IA-220-U2 a powerful CSP?
Built with the latest PCIe Gen4 interface, the IA-220-U2 can transfer up to twice the bandwidth of Gen3 devices.
NoLoad provides FPGA IP and host components. For the FPGA IP, you can see in the diagram the main components. More details are below:
As a complete solution, NoLoad provides host software with a choice of implementation:
The HRG gives you much more detail about the card such as block diagrams, tables and descriptions.
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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
BittWare On-Demand Webinar Computational Storage: Bringing Acceleration Closer to Data High-performance storage is changing as acceleration moves closer to storage and traditional form factors change
White Paper Introduction to BittWare’s SmartNIC Shell for Network Packet Processing Overview SmartNIC Shell is a complete working NIC that is implemented on a BittWare
BittWare Webinar Arkville PCIe Gen4 Data Mover Using Intel® Agilex™ FPGAs Webinar The Arkville IP from Atomic Rules was recently updated to support Intel Agilex