A High Throughput FPGA-Based Floating Point Conjugate Gradient Implementation for Dense Matrices
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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With a multitude of technological innovations, one emerging trend in image processing, and medical image processing, in particular, is custom hardware implementation of computationally intensive algorithms in the quest to achieve real-time performance. For reasons of area-efficiency and performance, these implementations often employ limited-precision datapaths. Identifying effective wordlengths for these datapaths while accounting for tradeoffs between design complexity and accuracy is a critical and time consuming aspect of this design process. Having access to optimized tradeoff curves can equip designers to adapt their designs to different performance requirements and target specific devices while reducing design time. This paper presents a multiobjective optimization strategy developed in the context of field-programmable gate array–based implementation of medical image registration. Within this framework, we compare several search methods and demonstrate the applicability of an evolutionary algorithm–based search for efficiently identifying superior multiobjective tradeoff curves. This strategy can easily be adapted to a wide range of signal processing applications, including areas of image and video processing beyond the medical domain.