
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.
Seamlessly transport data between FPGA logic and host memory at up to 60 GBytes/s (480 Gbps) in each direction. Arkville provides a high-throughput, low-latency conduit between host memory and FPGA fabric logic, offloading CPU core usage, eliminating memory copies, and improving overall efficiency.
View a demo of Arkville 17.05 performance plots.
As shown in the block diagram, Arkville has both a hardware and software component. The hardware component is an IP core that resides in the FPGA, producing and consuming AXI streams of packets making ingress or egress. The software component is a DPDK PMD “net/ark”, the Arkville DPDK poll-mode driver. Arkville is a conduit between FPGA logic and Host user memory for bulk data movement or individual packets.
Together, an Arkville solution looks to software like a “vanilla” line rate agnostic FPGA-based NIC (without any specific MAC). DPDK applications do not need to change significantly in order to enjoy the advantages of FPGA hardware acceleration.
GPP/Software Specific
Atomic Rules provides Arkville example designs that may be used as a starting point for your own solutions. These include:
Device | Speed | 6LUTs | FFs | M20k | Fmax |
---|---|---|---|---|---|
Intel Agilex F-Series | -2 | 81K | 220K | 250 | 500 |
Provider of FPGA IP components and solutions for networking and quality-of-time. IP offerings include Arkville data mover for high-throughput, low-latency communications between host memory and FPGA fabric logic.
Our technical sales team is ready to provide availability and configuration information, or answer your technical questions.
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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.
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