A Fine-grained Methodology for Accuracy-configurable and Energy-efficient Gaussian Filters Design - Borges, Talita Alves; Soares, Leonardo Bandeira; Meinhardt, Cristina (Symposium on Integrated Circuits and Systems Design (SBCCI), 2020)
Configurable architectures are alternatives to attend the quality and energy requirements of image and video processing dynamically. Together with approximate computing techniques, these approaches allow for energy-efficient digital systems regarding the error-tolerant applications. This paper proposes a fine-grained methodology to deal with multiple power-performance-quality requirements needed in nowadays digital Complementary Metal-Oxide-Semiconductor (CMOS) design. The main objectives can be observed as follows: i) to perform a design-space exploration through the bit-width reduction of the 2D Gaussian filter, ii) to develop approximate 2D Gaussian filter architectures during the design-time which serves as a proof of concept, and iii) to design a run-time accuracy-configurable 2D Gaussian filter accelerator. The application quality analysis is performed by adopting scripts coded in Python language. All the design-time approximate plus the accuracy-configurable architectures were described in Very High-Speed Integrated Circuits Hardware Description Language (VHDL) and synthesized for Application Specific Integrated Circuit (ASIC) implementation. The results for a 65nm cell-based technology show energy per operation reductions ranging from 9.87% to 59% with bit widths ranging from 7 to 3 bits, respectively. The extra hardware components to ensure the run-time configurable behavior implies up to 14% of area increasing.