Kung Fu Data Energy - Minimizing Communication Energy in FPGA Computations

Abstract

The energy in FPGA computations can be dominated by data communication energy, either in the form of memory references or data movement on interconnect (e.g., over 75% of energy for single processor Gaussian Mixture Modeling, Window Filtering, and FFT). In this paper, we explore how to use data placement and parallelism to reduce communication energy. We further introduce a new architecture for embedded memories, the Continuous Hierarchy Memory (CHM), and show that it increases the opportunities to reduce energy by strategic data placement. For three common FPGA tasks in signal and image processing (Gaussian Mixture Modeling, Window Filters, and FFTs), we show that data movement energy can vary over a factor of 9. The best solutions exploit parallelism and hierarchy and are 1.8-6.0× more energy-efficient than designs that place all data in a large memory bank. With the CHM, we can get an additional 10% improvement for full voltage logic and 30-80% when operating the computation at reduced voltage.