Efficient mapping of advanced signal processing algorithms on multi-processor architectures

Manjunath, B.B.; Williams, Alexandra; Chakrabarti, Chaitali; Papandreou-Suppappola, Antonia

doi:10.1109/sips.2008.4671774

Cited by 5 publications

(17 citation statements)

References 7 publications

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“…In this work, we propose a parallel algorithm for PF that integrates the parallel PF (PPF) algorithm in [45] with the IMH sampler. When mapped to hardware, this algorithm reduces the communication between the parallel processing element and the central unit significantly.…”

Section: Parallel Pf For Real-time Performancementioning

confidence: 99%

“…If the PF is implemented by computing the systematic resampling in the CU, all the importance weights would have to be transferred, resulting in a huge communication overhead. In [45], a method to significantly reduce the amount of data communication overhead was proposed. The main idea was to divide the particles into several groups in each PE and use the average of each group as the new particle.…”

Section: Ppf-imh Hardware Implementationmentioning

confidence: 99%

“…where ⌈a⌉ represents the smallest integer greater than a [45]. The parameter δ provides the range of each group; if δ is large, then the number of groups in each PE is small and thus the algorithm 13 precision is low.…”

Section: Ppf-imh Hardware Implementationmentioning

confidence: 99%

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Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Liu

Zhang

Chakrabarti

et al. 2013

IEEE Trans. Signal Process.

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Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems.First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance.We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking.For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. i Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate com...

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Section: Parallel Pf For Real-time Performancementioning

confidence: 99%

Section: Ppf-imh Hardware Implementationmentioning

confidence: 99%

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Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Liu

Zhang

Chakrabarti

et al. 2013

IEEE Trans. Signal Process.

Self Cite

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“…Recently, the idea of independent Metropolis-Hastings (IMH) algorithm [8] is utilized to facilitate the parallel design of the resampling procedure in PF [9,10]. In conclusion, to enhance the parallelized PF, the studies in [7][8][9][10][11] focus on the modification of the resampling operation.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the proposed MP-PF improves the execution time of the parallelized PF by reducing the complexity of the resampling procedure. It should be noted that our approach is not proposed to replace the algorithms in [7][8][9][10][11]. Proposed MP-PF can be combined with modified resampling algorithms in [7][8][9][10][11] to further improve the efficiency of the parallelized PF.…”

Section: Introductionmentioning

confidence: 99%

Multi-prediction particle filter for efficient parallelized implementation

Chu

Chao

et al. 2011

EURASIP J. Adv. Signal Process.

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Particle filter (PF) is an emerging signal processing methodology, which can effectively deal with nonlinear and non-Gaussian signals by a sample-based approximation of the state probability density function. The particle generation of the PF is a data-independent procedure and can be implemented in parallel. However, the resampling procedure in the PF is a sequential task in natural and difficult to be parallelized. Based on the Amdahl's law, the sequential portion of a task limits the maximum speed-up of the parallelized implementation. Moreover, large particle number is usually required to obtain an accurate estimation, and the complexity of the resampling procedure is highly related to the number of particles. In this article, we propose a multi-prediction (MP) framework with two selection approaches. The proposed MP framework can reduce the required particle number for target estimation accuracy, and the sequential operation of the resampling can be reduced. Besides, the overhead of the MP framework can be easily compensated by parallel implementation. The proposed MP-PF alleviates the global sequential operation by increasing the local parallel computation. In addition, the MP-PF is very suitable for multi-core graphics processing unit (GPU) platform, which is a popular parallel processing architecture. We give prototypical implementations of the MP-PFs on multi-core GPU platform. For the classic bearing-only tracking experiments, the proposed MP-PF can be 25.1 and 15.3 times faster than the sequential importance resampling-PF with 10,000 and 20,000 particles, respectively. Hence, the proposed MP-PF can enhance the efficiency of the parallelization.

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Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

Liu

Zhang

Chakrabarti

et al. 2011

J Sign Process Syst

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Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH) resampling algorithms to improve root mean-squared estiThe parallel particle filter implementation was discussed in our 2010 IEEE Workshop on Signal Processing Systems paper [1]. This work also presents: the new algorithm and hardware implementation described in more detail (Section 3); the effect of the number of processing elements and number of groups in each processing element on the parallel particle filter algorithm performance (Sections 5.2 and 6.1); the waveform-agile sensing algorithm (Section 4.1), the waveform-agile tracking application (Section 4.2), and its hardware (FPGA) implementation (Section 4.3); new simulation and hardware implementation results on waveform-agile tracking (Sections 5.4 and 6.2). mation error (RMSE) performance. We implement the new PPF-IMH algorithm on a Xilinx Virtex-5 field programmable gate array (FPGA) platform. For a one-dimensional problem with 1,000 particles, the PPF-IMH architecture with four processing elements uses less than 5% of a Virtex-5 FPGA's resource and takes 5.85 μs for one iteration. We also incorporate waveform-agile tracking techniques into the PPF-IMH algorithm. We demonstrate a significant performance improvement when the waveform is adaptively designed at each time step with 6.84 μs FPGA processing time per iteration.

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Efficient mapping of advanced signal processing algorithms on multi-processor architectures

Cited by 5 publications

References 7 publications

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Multi-prediction particle filter for efficient parallelized implementation

Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

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