Xin Yao, Li Will X Y, Zhang Zhaorui, Cheung Ray C C, Song Dong, Berger Theodore W
IEEE/ACM Trans Comput Biol Bioinform. 2015 Sep-Oct;12(5):1034-47. doi: 10.1109/TCBB.2015.2440248.
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.
神经编码是神经假体设计的一个重要过程，其中自适应滤波器已被广泛应用。在实际应用中，当神经元模型、条件或系统要求发生变化时，需要在不同的滤波器之间进行切换，这些切换可以基于连续观测或点过程。作为神经假体编码芯片的候选方案，低功耗通用处理器在计算效率上并不高，特别是对于大规模神经群体编码。专用集成电路（ASIC）在不同自适应滤波器之间切换时缺乏灵活性，同时设计和制造的成本也很高。在这项研究工作中，我们探索了一种用于神经解码活动中自适应滤波器的专用指令集处理器（ASIP）。所提出的架构专注于对常用自适应滤波器中最耗时的矩阵/向量运算进行高效计算，能够同时提供灵活性和吞吐量。通过评估和实现结果表明，所提出的ASIP设计在面积上是高效的，并且在计算性能上与商用CPU具有竞争力。
