Resources for specific applications of our Data Center products.
An Open Source FPGA CNN Library (ad-an-0055)
Convolutional neural networks have become the core component of a large number of hyperscale deployed machine learning algorithms used in image and vision recognition tasks. Low-bitwidth FPGA implementations of these networks provide a potential path to higher throughput and lower power machine learning inference solutions.
One purpose of this toolbox is to provide an easy path for developers to investigate the accuracy implications of switching from floating point defined network weights to low bitwidth fixed point weights on each of the layers making up the network.
Another purpose of this toolbox is to provide tools and knowledge for comparing network structure and size with on-chip memory and processing capabilities of FPGAs to aid in optimal device selection.
FPGA based Volume Ray-Casting (vrc_2017_final_2)
This paper outlines how a compute-intensive algorithm can be implemented on a FPGA card with a PCI Express interface, utilising Vivado HLS and Alpha Data’s ADB3 PCIe Bridge. The presented system is shown to be scalable and power efficient.
The FPGA platform is built from a combination of a top-level IP Integrator (IPI) system, IP cores for on/off-chip data flow and board-specific host communications. These are combined with the ray-casting IP cores written in C++ and synthesised with Xilinx’s Vivado HLS tool. Some important aspects of these IP are discussed.
Contact Alpha Data for further details : Email email@example.com.