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Signal Processing on Ambric Processor Array : Baseband processing in radio base stationsQasim, Muhammad, Majid Ali, Chaudhry January 2008 (has links)
<p>The advanced signal processing systems of today require extreme data throughput and low power consumption. The only way to accomplish this is to use parallel processor architecture.</p><p>The aim of this thesis was to evaluate the use of parallel processor architecture in baseband signal processing. This has been done by implementing three demanding algorithms in LTE on Ambric Am2000 family Massively Parallel Processor Array (MPPA). The Ambric chip is evaluated in terms of computational performance, efficiency of the development tools, algorithm and I/O mapping.</p><p>Implementations of Matrix Multiplication, FFT and Block Interleaver were performed. The implementation of algorithms shows that high level of parallelism can be achieved in MPPA especially on complex algorithms like FFT and Matrix multiplication. Different mappings of the algorithms are compared to see which best fit the architecture.</p>
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Signal Processing on Ambric Processor Array : Baseband processing in radio base stationsQasim, Muhammad, Majid Ali, Chaudhry January 2008 (has links)
The advanced signal processing systems of today require extreme data throughput and low power consumption. The only way to accomplish this is to use parallel processor architecture. The aim of this thesis was to evaluate the use of parallel processor architecture in baseband signal processing. This has been done by implementing three demanding algorithms in LTE on Ambric Am2000 family Massively Parallel Processor Array (MPPA). The Ambric chip is evaluated in terms of computational performance, efficiency of the development tools, algorithm and I/O mapping. Implementations of Matrix Multiplication, FFT and Block Interleaver were performed. The implementation of algorithms shows that high level of parallelism can be achieved in MPPA especially on complex algorithms like FFT and Matrix multiplication. Different mappings of the algorithms are compared to see which best fit the architecture.
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Implementation and Evaluation of MPEG-4 Simple Profile Decoder on a Massively Parallel Processor ArraySavas, Suleyman January 2011 (has links)
The high demand of the video decoding has pushed the developers to implement the decoders on parallel architectures. This thesis provides the deliberations about the implementation of an MPEG-4 decoder on a massively parallel processor array (MPPA), Ambric 2045, by converting the CAL actor language implementation of the decoder. This decoder is the Xilinx model of the MPEG-4 Simple Profile decoder and consists of four main blocks; parser, acdc, idct2d and motion. The parser block is developed in another thesis work [20] and the rest of the decoder, which consists of the other three blocks, is implemented in this thesis work. Afterwards, in order to complete the decoder, the parser block is combined with the other three blocks. Several methods are developed for conversion purposes. Additionally, a number of other methods are developed in order to overcome the constraints of the ambric architecture such as no division support. At the beginning, for debugging purposes, the decoder is implemented on a simulator which is designed for Ambric architecture. Finally the implementation is uploaded to the Ambric 2045 chip and tested with different input streams. The performance of the implementation is analyzed and satisfying results are achieved when compared to the standards which are in use in the market. These performance results can be considered as satisfying for any real-time application as well. Furthermore, the results are compared with the results of the CAL implementation, running on a single 2GHz i7 intel processor, in terms of speed and efficiency. The Ambric implementation runs 4,7 times faster than the CAL implementation when a small input stream (300 frames with resolution of 176x144) is used. However, when a large input stream (384 frames with resolution of 720x480) is used, the Ambric implementation shows a performance which is approximately 32 times better than the CAL implementation, in terms of decoding speed and throughput. The performance may increase further together with the size of the input stream up to some point.
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