This dissertation addressed the bandwidth issue from the source to the destination of data transfers based on the core concept of facilitating the address and data correlation among accesses. At the source of data transfers, this dissertation proposed a memory controller that increased the bandwidth utilization by facilitating access address correlation, taking the advantage of new advanced data transfer protocol, and the characteristics of external memories. After improving the bandwidth utilization at the source of data transfers, this dissertation focused on improving the bandwidth utilization of a bus interconnect adopting advanced protocol under the traditional share-link topology. Finally, the bandwidth requirement reduction techniques based on data and access characteristics have been studied at the destination of data transfers. The bandwidth requirement can be reduced in two major ways. The first approach is to take the advantage of data characteristics. The other approach is to reuse data based on an algorithm’s data access spatial and temporal locality. In video coding, the CFMMC architecture was capable of reducing the bandwidth requirement and energy consumption up to 72% and 16% respectively when the percentage of perfect matched macroblocks is higher than 70%. In early vision tasks, the proposed PUPP reduced the bandwidth to the image memory by 81.6% in the proposed meanshift architecture. Both CFMMC and PUPP were examples of the first approach to reduce bandwidth requirement. In the MCADSW stereo matching architecture, the proposed the PCR and AREW techniques, which were examples of the second bandwidth requirement reduction approach, could reduce bandwidth requirement by an order.
Although this dissertation has proposed methods to increase system’s effective bandwidth and to reduce core’s bandwidth requirement for video and vision applications, systematic integration of these techniques into advanced ESL design flow tools has not been
available. With the proposed bandwidth issue solutions, future researches can consider integrating these solutions into an automatic bandwidth optimizing tool. Doing so would enable more complex bandwidth demanding but extremely useful video and vision algorithms to be accelerated for real-time applications.
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