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 Enyx Development Framework (nxFramework) is a hardware and software development environment designed to efficiently build and maintain ultra-low latency FPGA applications for the financial industry. Based on 10 years of research and development, nxFramework is the foundation for all Enyx off-the-shelf solutions and provides clients with the toolchain to manage a large portfolio of applications.
Developed for building in-house high performance trading engines, order execution systems, pre-trade risk check gateways, and custom projects — any skilled FPGA developer starting a new low latency project, maintaining an existing one, or looking to change platforms can immediately reduce their time-to-production with nxFramework.
Ultra-low latency connectivity cores
Library of 60+ utility cores
Provided with Core
Simulation tool used:
QuestaSim (contact IntelliProp for latest versions supported)
Support:
Phone and email support will be provided for fully licensed cores for a period of 6 months from the delivery date.
Notes:
Other simulators are available. Please contact IntelliProp for more information.
Enables simple configuration and monitoring of Enyx connectivity & utility cores, including interaction with the FPGA application via our C/C++ libraries.
A Python scripted development environment that enables users to simplify their development cycle and accelerate their time-to-production.
Equipped with a web-based GUI that can configure and monitor the FPGA at runtime, allowing for quick deployment and debug.
ULL Tick-to-trade platform
Pre-trade Risk Check Gateway
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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Explore using oneAPI with our 2D FFT demo on the 520N-MX card featuring HBM2. Be sure to request the code download at the bottom of the page!
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