A large number of algorithms for multidimensional signals processing and scientific computation come in the form of iterative stencil loops (ISLs), whose data dependencies span across multiple iterations. Because of their complex inner structure, automatic hardware acceleration of such algorithms is traditionally considered as a difficult task. In this paper, we introduce an automatic design flow that identifies, in a wide family of bidimensional data processing algorithms, sub-portions that exhibit a kind of parallelism close to that of ISLs; these are mapped onto a space of highly optimized ad-hoc architectures, which is efficiently explored to identify the best implementations with respect to both area and throughput. Experimental results show that the proposed methodology generates circuits whose performance is comparable to that of manually-optimized solutions, and orders of magnitude higher than those generated by commercial HLS tools.