Targeted for highly sophisticated visual signal processing, we introduce in this paper complexity metrics or measures of algorithms which featuring architectural information are feedback or back annotated in early design stages to facilitate concurrent exploration of both algorithmic and architectural optimizations. With application to 3D spatio-temporal motion estimation for video coding, we have demonstrated significant reduction in design cost while the algorithmic performance still surpasses recent published works and even full search under many circumstances. Moreover, we have also shown the importance and substantiality of this complexity analysis technique in the extraction of features common to various different de-interlacing algorithms adapted for versatile video content, in designing highly efficient reconfigurable video architectures. As such this novel algorithm/architecture co-exploration methodology forms the basis for dataflow models with more accurate software/hardware partitioning resulting in multi-million gate and/or instruction software simulation platforms and fast prototyping hardware platforms for the next generation electronic system level design of SoCpsilas.
['Gwo Giun Lee', 'Ming-Jiun Wang', 'He-Yuan Lin', 'Ron-Lai Lai']
On the efficient algorithm/architecture co-exploration for complex video processing