Oliver Williams

BLAS Comparison on FPGA, CPU and GPU

High Performance Computing (HPC) or scientific codes are being executed across a wide variety of computing platforms from embedded processors to massively parallel GPUs. We present a comparison of the Basic Linear Algebra Subroutines (BLAS) using double-precision floating point on an FPGA, CPU and GPU. On the CPU and GPU, we utilize standard libraries on state-of-the-art devices. On the FPGA, we have developed parameterized modular implementations for the dot-product and Gaxpy or matrix-vector multiplication. In order to obtain optimal performance for any aspect ratio of the matrices, we have designed a high-throughput accumulator to perform an efficient reduction of floating point values. To support scalability to large data-sets, we target the BEE3 FPGA platform. We use performance and energy efficiency as metrics to compare the different platforms. Results show that FPGAs offer comparable performance as well as 2.7 to 293 times better energy efficiency for the test cases that we implemented on all three platforms.

An Adaptive Machine Director

We model the class of problem faced by a video broadcast director, who must act as an active perception agent to select a view of interest to a human from a range of possibilities. Real-time learning of a broadcast direction policy is achieved by efficient online Bayesian learning of the model’s parameters based on intermittent user feedback. In contrast to existing machine direction systems, which are dedicated to a particular scenario, our novel approach allows flexible learning of direction policies for novel domains or for viewerspecific preferences. We illustrate the flexibility of our approach by applying our model to a selection of scenarios with audio-visual input including teleconferencing, meetings and dance entertainment.