Presenter

Mr Bruno Tiago da Silva Gomes. - INDI-VUB [Email]

Abstract

Field-Programmable Gate Arrays (FPGAs) increasingly assume roles as hardware accelerators which significantly speed up computations in a wide range of streaming applications. For instance, specific streaming applications related to audio or image processing also demand high performance, runtime dynamism and power efficiency. Such applications demand a low latency while presenting a large amount of parallelism, both well-known features offered by FPGAs nowadays. Although the flexibility offered by FPGAs allows to implement customized architectures with higher computational performance and better power efficiency than multi-core CPUs and GPUs respectively, the design of such architectures is a very time-consuming task. Moreover, heterogeneous FPGA-based platforms and devices can only be fully exploited when modelling and analysing architectures combining the best of each technology.
The aim of this thesis is the acceleration of streaming applications by overcoming the challenges that the available FPGA-based systems present when mapping high-performance demanding streaming applications. On the one hand, performance analysis and techniques are proposed to exploit customized architectures for acoustic streaming applications demanding a real-time computation of the incoming signals from dense microphone arrays.
The proposed design-space exploration of reconfigurable architectures, including a complete analysis of the different trade-offs in terms of performance, power and frequency response, leads to designs providing the dynamic response, the high performance or the power efficiency demanded by highly constrained applications such as acoustic beamforming. On the other hand, heterogeneous FPGA-based systems performance models such as the roofline model are adapted for FPGAs to guide the design methodology to reach the highest performance. High-Level Synthesis tools are used not only as a complement of our roofline model but also for performance prediction. These models are applied to accelerate simple convolutional image filters and a more complex image algorithm for pedestrian detection.