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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics
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An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics

机译：神经假肢中自适应滤波器的专用指令集处理器（ASIP）

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摘要

Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.
机译：神经编码是神经假体设计的必要过程，其中自适应滤波器已被广泛使用。在实际应用中，当神经元模型，条件或系统要求发生变化时，可能需要基于连续观察或点过程在不同的过滤器之间进行切换。作为神经假体的编码芯片的候选者，低功率通用处理器在计算上并不高效，特别是对于大规模的神经种群编码而言。专用集成电路（ASIC）不具有在不同的自适应滤波器之间切换的灵活性，而设计和制造的成本却很高。在这项研究工作中，我们探索了神经解码活动中自适应滤波器的专用指令集处理器（ASIP）。所提出的体系结构集中于常用自适应滤波器中最耗时的矩阵/矢量运算的有效计算，能够提供灵活性和吞吐量。提供的评估和实施结果证明了所提出的ASIP设计具有区域效率，同时在计算性能上与商用CPU竞争。

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