Energy-efficient acceleration of big data analytics applications using FPGAs

A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization. Considering the time dedicated to create such hardware implementations, an analysis that estimates how much benefit we gain in terms of speed and energy efficiency, through offloading various functions to hardware would be necessary. This work analyzes data mining and machine learning algorithms, which are utilized extensively in big data applications in a heterogeneous CPU+FPGA platform. We select and offload the computational intensive kernels to the hardware accelerator to achieve the highest speed-up and best energy-efficiency. We use the latest Xilinx Zynq boards for implementation and result analysis. We also perform a first order comprehensive analysis of communication and computation overheads to understand how the speedup of each application contributes to its overall execution in an end-to-end Hadoop MapReduce environment. Moreover, we study how other system parameters such as the choice of CPU (big vs little) and the number of mapper slots affect the performance and power-efficiency benefits of hardware acceleration. The results show that a kernel speedup of upto χ 321.5 with hardware+software co-design can be achieved. This results in χ2.72 speedup, 2.13χ power reduction, and 15.21χ energy efficiency improvement (EDP) in an end-to-end Hadoop MapReduce environment.