The design of most high-performance and heterogeneous processing platforms is usually solely focused on the computational part, while neglecting the power/performance impact of the data-management infrastructures. Moreover, such systems often struggle to achieve their potential performance when applications require fetching data with complex memory access patterns. To overcome these issues, a energy-efficient stream-based data-management infrastructure is herein proposed, relying on a novel tree-based descriptor specification. Such descriptors are decoded by a Descriptor Tree Controller (DTC) architecture, which allows simple and efficient management of arbitrarily complex memory access patterns. Moreover, a Stream Management Engine (SME) ensures energy-efficient data-reutilization through the application of automatic stream rerouting, splitting and merging techniques. The obtained results show that the proposed DTC architecture is capable of a highly efficient complex data-pattern generation, while significantly reducing the size occupied by the pattern description, when compared with state-of-the-art approaches. By also enabling the deployment of data-reuse techniques, a reduction of up to 85× in the number of accesses to the main shared memory is achieved, resulting in a decrease as high as 475× in the observed energy consumption.
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