Investigating the Dirac operator evaluation with FPGAs

In recent years the computational capacity of single Field Programmable Gate Arrays (FPGA) devices as well as their versatility has increased significantly. Adding to that the High Level Synthesis frameworks allowing to program such processors in a high level language like C++, makes modern FPGA devices a serious candidate as building blocks of a general purpose High Performance Computing solution. In this contribution we describe benchmarks which we performed using a Lattice QCD code, a highly compute-demanding HPC academic code for elementary particle simulations. We benchmark the performance of a single FPGA device running in two modes: using the external or embedded memory. We discuss both approaches in detail using the Xilinx U250 device and provide estimates for the necessary memory throughput and the minimal amount of resources needed to deliver optimal performance depending on the available hardware platform.Comment: 8 pages, 5 figure

High throughput accelerator interface framework for a linear time-multiplexed FPGA overlay

Coarse-grained FPGA overlays improve design productivity through software-like programmability and fast compilation. However, the effectiveness of overlays as accelerators is dependent on suitable interface and programming integration into a typically processor-based computing system, an aspect which has often been neglected in evaluations of overlays. We explore the integration of a time-multiplexed FPGA overlay over a server-class PCI Express interface. We show how this integration can be optimised to maximise performance, and evaluate the area overhead. We also propose a user-friendly programming model for such an overlay accelerator system

Real-time human action recognition on an embedded, reconfigurable video processing architecture

Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd

FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture

In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. ``motion history image") class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments

Reconfigurable Logic Embedded Architecture of Support Vector Machine Linear Kernel

Support Vector  Machine  (SVM) is a linear  binary classifier  that  requires a  kernel  function  to  handle  non-linear problems.  Most  previous  SVM  implementations for  embedded systems  in literature were  built  targeting a certain  application; where analyses were done through comparison  with software im- plementations only. The impact  of different  application datasets towards  SVM hardware performance were not analyzed.  In this work,  we propose  a parameterizable linear  kernel  architecture that  is fully pipelined.  It  is prototyped and  analyzed  on Altera Cyclone  IV  platform   and  results  are  verified  with  equivalent software  model.  Further analysis  is  done  on  determining the effect  of  the  number of  features   and  support   vectors  on  the performance of the  hardware architecture. From  our  proposed linear  kernel  implementation, the number of features  determine the maximum  operating frequency  and amount  of logic resource utilization,  whereas  the  number of support   vectors  determines the  amount  of on-chip  memory  usage  and  also the  throughput of the system