Methods and Tools for Real-Time Neural Image Processing

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As a rapidly developing form of bioengineering technology, neuromodulationsystems involve extracting information from signals that are acquired from the brain and utilizing the information to stimulate brain activity. Neuromodulation has the potential to treat a wide range of neurological diseases and psychiatric conditions, as well as the potential to improve cognitive function.

Neuromodulation integrates neural decoding and stimulation. As one of the twocore parts of neuromodulation systems, neural decoding subsystems interpret signals acquired through neuroimaging devices. Neuroimaging is a field of neuroscience that uses imaging techniques to study the structure and function of the brain and other central nervous system functions. Extracting information from neuroimaging signals, as is required in neural decoding, involves key challenges due to requirements of real-time, energy-efficient, and accurate processing and for large-scale, high resolution image data that are characteristic of neuromodulation systems.

To address these challenges, we develop new methods and tools for design andimplementation of efficient neural image processing systems. Our contributions are organized along three complementary directions. First, we develop a prototype system for real-time neuron detection and activity extraction called the Neuron Detection and Signal Extraction Platform (NDSEP). This highly configurable system processes neural images from video streams in real-time or off-line, and applies techniques of dataflow modeling to enable extensibility and experimentation with a wide variety of image processing algorithms.

Second,we develop a parameter optimization framework to tune the performance of neural image processing systems. This framework, referred to as the NEural DEcoding COnfiguration (NEDECO) package, automatically optimizes arbitrary collections of parameters in neural image processing systems under customizable constraints. The framework allows system designers to explore alternative neural image processing trade-offs involving execution time and accuracy. NEDECO is also optimized for efficient operation on multicore platforms, which allows for faster execution of the parameter optimization process.

Third, we develop a neural network inference engine targeted to mobile devices.The framework can be applied to neural network implementation in many application areas, including neural image processing. The inference engine, called ShaderNN, is the first neural network inference engine that exploits both graphics-centric abstractions (fragment shaders) and compute-centric abstractions (compute shaders). The integration of fragment shaders and compute shaders makes improved use of the parallel computing advantages of GPUs on mobile devices. ShaderNN has favorable performance especially in parametrically small models.