English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this project, we have successfully designed, implemented, deployed andtested a novel FPGA accelerated algorithm for neural network training. Thealgorithm itself was developed in an independent study option. This trainingmethod is based on Alternating Direction Method of Multipliers algorithm, whichhas strong parallel characteristics and avoids procedures such as matrixinversion that are problematic in hardware designs by employing LSMR. As anintermediate stage, we fully implemented the ADMM-LSMR method in C language forfeed-forward neural networks with a flexible number of layers and hidden size.We demonstrated that the method can operate with fixed-point arithmetic withoutcompromising the accuracy. Next, we devised an FPGA accelerated version of thealgorithm using Intel FPGA SDK for OpenCL and performed extensive optimisationstages followed by successful deployment of the program on an Intel Arria 10 GXFPGA. The FPGA accelerated program showed up to 6 times speed up comparing toequivalent CPU implementation while achieving promising accuracy.

An FPGA Accelerated Method for Training Feed-forward Neural Networks Using Alternating Direction Method of Multipliers and LSMR