Trends in multicore DSP platforms

Karam, Lina J.; AlKamal, Ismail; Gatherer, Alan; Frantz, Gene; Anderson, David V.; Evans, Brian L.

doi:10.1109/msp.2009.934113

Cited by 92 publications

(44 citation statements)

References 8 publications

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“…In that special issue, several authors describe "novel applications that can be enabled by platforms with multiple cores, and more extensive design examples of signal processing on platforms with multiple cores that demonstrate useful techniques for developing efficient implementations". Moreover, nowadays, DSP architectures are incorporating multi-core capacities [15]. The implementation of advanced algorithms able to use both architectures is crucial in MIMO research, since it allows to fully exploit the capabilities of the modern machine architectures and to reduce the response time of computationally expensive problems.…”

Section: Complex Conjugation (·) Hmentioning

confidence: 99%

MIMOPack: a high-performance computing library for MIMO communication systems

2014

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Nowadays, several communication standards are emerging and evolving, searching higher transmission rates, reliability and coverage. This expansion is primarily driven by the continued increase in consumption of mobile multimedia services due to the emergence of new handheld devices such as smartphones and tablets.One of the most significant techniques employed to meet these demands is the use of multiple transmit and receive antennas, known as MIMO (Multiple Input Multiple Output) systems. The use of this technology allows to increase the transmission rate and the quality of the transmission through the use of multiple antennas at the transmitter and receiver sides.MIMO technologies have become an essential key in several wireless and broadband standards such as Wireless Local Area Network (WLAN), Worldwide interoperability for Microwave Acces (WiMAX), Long Term Evolution (LTE) and Next Generation Handheld (DVB-NGH), for the reception of Digital Terrestrial Television (DTT) in handheld devices. These technologies will be incorporated also in future standards, therefore is expected in the coming years a great deal of research in this field.Clearly, the study of MIMO systems is critical in the current investigation, however the problems that arise from this technology are very complex. High Performance Computing (HPC) systems, and specifically, modern hardware architectures as multi-core and many-cores (e.g Graphics Processing Units (GPU)) are playing a key role in the development of efficient and low-complexity algorithms for MIMO transmissions. Proof of this is that the number of scientific contributions and research projects related to its use has increased in the last years. Also, some high performance libraries have been implemented as tools for researchers or companies involved in the development of future communication standards. Two of the most popular libraries are: IT++ that is a library based on the use of some optimized libraries for multi-core processors and the Communications System Toolbox designed for use with MATLAB and Simulink, which uses GPU computing. However, there is not a library able to run on a heterogeneous platform using all the available resources.In view of the high computational requirements in MIMO application research and the shortage of tools able to satisfy them, we have made a vi Abstract special effort to develop a library to ease the development of adaptable parallel applications in accordance with the different architectures of the executing platform. The library, called MIMOPack, aims to implement efficiently using parallel computing, a set of functions to perform some of the critical stages of MIMO communication systems simulation.The main contribution of the thesis is the implementation of efficient Hard and Soft output detectors, since the detection stage is considered the most complex part of the communication process. These detectors are highly configurable and many of them include preprocessing techniques that reduce the computational cost and increase the perf...

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Section: Complex Conjugation (·) Hmentioning

confidence: 99%

MIMOPack: a high-performance computing library for MIMO communication systems

2014

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“…The processor industry stepped to multi-core architectures in the second decade of the 21st century [12][13]. The multi-core approach tried to solve the problem of higher computations while staying low power, but it produced a lot of new problems, including algorithm parallelization, multi-core programming and parallel threads synchronization.…”

Section: Introductionmentioning

confidence: 99%

Teaching multi-core DSP implementation on EVM C6678 board

Kharin

Vityazev

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Teaching implementation of digital signal processing systems plays a very important role in recent technical education. The multi-core digital signal processor (DSP) is a new type of architecture widely used now in the industry. A new course on multi-core DSP programming is considered in this paper. The lab experiments are described. The course has been developed for the TMS320C6678 multicore DSPs. This paper provides educators with a content that cover theoretical and technical skills that are required by industry.

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“…Two major issues in multiprocessing are a) the nature of the interconnect topology [2] and b) the scheduling and synchronization of computations. In fact, these two issues can not be separated from each other, as the interconnect dictates the nature of the scheduling approach.…”

Section: Introductionmentioning

confidence: 99%

Applying the adaptive Hybrid Flow-Shop scheduling method to schedule a 3GPP LTE physical layer algorithm onto many-core digital signal processors

Heulot

Boutellier

Pelcat

et al. 2013

2013 NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2013)

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Abstract-Currently, Multicore Digital Signal Processor (DSP) platforms are commonly used in telecommunications baseband processing. In the next few years, high performance DSPs are likely to combine many more DSP cores for signal processing with some General-Purpose Processor (GPP) cores for application control. As the number of cores increases in new DSP platform designs, scheduling of applications is becoming a complex operation. Meanwhile, the variability of the scheduled applications also tends to increase as applications become more sophisticated. Such variations require runtime adaptivity of application scheduling. This paper extends the previous work on adaptive scheduling by using the Hybrid Flow-Shop (HFS) scheduling method, which enables the device architecture to be modeled as a pipeline of Processing Elements (PEs) with multiple alternate PEs for each pipeline stage. HFS scheduling is applied to the scheduling of 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) telecommunication standard Uplink Physical Layer data processing (PUSCH).The experiments, conducted on an ARM Cortex-A9 GPP, show that an HFS scheduling algorithm has an overhead that increases very slowly with the number of PEs. This makes the method suitable for executing the adaptive scheduling in less than 1 ms for the 501 actors of a LTE PUSCH dataflow description executed on a 256-core architecture.

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Trends in multicore DSP platforms

Cited by 92 publications

References 8 publications

MIMOPack: a high-performance computing library for MIMO communication systems

MIMOPack: a high-performance computing library for MIMO communication systems

Teaching multi-core DSP implementation on EVM C6678 board

Applying the adaptive Hybrid Flow-Shop scheduling method to schedule a 3GPP LTE physical layer algorithm onto many-core digital signal processors

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