A Graph-Based Accelerator of Retinex Model With Bit-Serial Computing for Image Enhancements

This work proposes the Poisson equation formulation of the Retinex model for image enhancements using a low-power graph hardware accelerator performing finite difference updates on a lattice graph processing element (PE) array. By encapsulating the underlying algorithm in a graph hardware structure, a highly localized dataflow that takes advantage of the physical placement of the PEs is enabled to minimize data movement and maximize data reuse. The on-chip dataflow that achieves data sharing, and reuse among neighboring PEs during massively parallel updates is generated in each PE driven by two external control signals. Using a custom accumulator design intended for bit-serial computing, this work enables precision on demand and extensive on-chip data reuse with minimal area overhead, accommodating a non-overlap image mapping scheme in which a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times 20$ </tex-math></inline-formula> image tile can be processed without external memory access at a time. With increasing user-configurable update count, image noise and shadow can be progressively removed with the inevitable loss of image details. Fabricated using a 65nm technology, the test chip occupies 0.2955mm2 core area and consumes 2.191mW operating at 1V, 25.6MHz, and a reconfigurable 10- or 14-bit precision.