Energy efficient and runtime based approximate computing techniques for image processing applications: an integrated approach covering circuit to algorithmic level - Huang, Junqi (2021)
Approximate computing has been widely used in error resilient design for improving the energy performance by reducing circuit complexity and allowing circuits to produce acceptable error results (approximation). Generally, the approximate computing techniques have been developed and implemented either at algorithmic level or logic level or circuit level and with no feasibility of on-the-fly or runtime change of approximation. Thus, different from the existing methods, this thesis presents novel energy-efficient integrated approach of implementing approximate computing techniques from circuit level to the algorithmic level that incorporate the change of approximation at runtime without incurring any extra hardware requirement. The two new techniques proposed are namely frequency upscaling (FUS) technique and voltage overscaling (VOS) technique. These two new techniques developed for the logic/circuit level abstract are integrated into two new proposed algorithmic level approximate computing techniques known as zigzag low-complexity approximate DCT (ZLCADCT) technique for image compression and approximate Newton method for image denoising. Thus, developing an integrated approach of implementing runtime based approximate computing techniques from circuit level abstract to algorithmic level abstract for image processing applications.