1,721,205 research outputs found

    Improving Network-on-Chip-based Turbo Decoder Architectures

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    In this work novel results concerning Networkon- Chip-based turbo decoder architectures are presented. Stemming from previous publications, this work concentrates first on improving the throughput by exploiting adaptive-bandwidth-reduction techniques. This technique shows in the best case an improvement of more than 60 Mb/s. Moreover, it is known that double-binary turbo decoders require higher area than binary ones. This characteristic has the negative effect of increasing the data width of the network nodes. Thus, the second contribution of this work is to reduce the network complexity to support doublebinary codes, by exploiting bit-level and pseudo-floatingpoint representation of the extrinsic information. These two techniques allow for an area reduction of up to more than the 40 % with a performance degradation of about 0.2 d

    Turbo NOC: a framework for the design of Network-on-Chip-basedturbo decoder architectures

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    This paper proposes a general framework for the design and simulation of network-on-chip-based turbo decoder architectures. Several parameters in the design space are investigated, namely, network topology, parallelism degree, the rate at which messages are sent by processing nodes over the network, and routing strategy. The main results of this analysis are as follows: 1) the most suited topologies to achieve high throughput with a limited complexity overhead are generalized de Bruijn and generalized Kautz topologies and 2) depending on the throughput requirements, different parallelism degrees, message injection rates, and routing algorithms can be used to minimize the network area overhead
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