An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics

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, o...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 12; no. 5; pp. 1034 - 1047
Main Authors: Yao Xin, Li, Will X. Y., Zhaorui Zhang, Cheung, Ray C. C., Dong Song, Berger, Theodore W.
Format: Journal Article
Language:English
Published: United States IEEE 01.09.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Action Potentials - physiology
Adaptation models
adaptive filter
Adaptive filters
Algorithms
application specific instruction set processor
Brain - physiology
Brain Mapping - instrumentation
Brain models
Brain-Computer Interfaces
Coding
Computation
Computational efficiency
Computer architecture
Design engineering
Electroencephalography - instrumentation
Encoding
Equipment Design
Equipment Failure Analysis
field-programmable gate array
Flexibility
Hardware
Humans
Microprocessors
neural coding
Neural Prostheses
Neurons
neuroprosthetics
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted - instrumentation
Switches
Neuroprosthetics
application specific instruction set processor
adaptive filter
field-programmable gate array
neural coding
ISSN:1545-5963, 1557-9964
Online Access:Get full text
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Summary: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.
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2015.2440248