The main objective of this thesis is to propose new methods for designing high-performance embedded computer system architectures. To achieve the goal, three major components - multi-core processing elements (PEs), DRAM main memory systems, and on/off-chip interconnection networks - in multi-processor embedded systems are examined in each section respectively.
The first section of this thesis presents architectural enhancements to graphics processing units (GPUs), one of the multi- or many-core PEs, for improving performance of embedded applications. An embedded application is first mapped onto GPUs to explore the design space, and then architectural enhancements to existing GPUs are proposed for improving throughput of the embedded application.
The second section proposes high-performance buffer mapping methods, which exploit useful features of DRAM main memory systems, in DSP multi-processor systems. The memory wall problem becomes increasingly severe in multiprocessor environments because of communication and synchronization overheads. To alleviate the memory wall problem, this section exploits bank concurrency and page mode access of DRAM main memory systems for increasing the performance of multiprocessor DSP systems.
The final section presents a network-centric Turbo decoder and network-centric FFT processors. In the era of multi-processor systems, an interconnection network is another performance bottleneck. To handle heavy communication traffic, this section applies a crossbar switch - one of the indirect networks - to the parallel Turbo decoder, and applies a mesh topology to the parallel FFT processors. When designing the mesh FFT processors, a very different approach is taken to improve performance; an optical fiber is used as a new interconnection medium.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/42766 |
| Date | 26 August 2011 |
| Creators | Lee, Dongwon |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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