Event
Ph.D. Dissertation Defense: Jing Xie
Wednesday, August 9, 2023
3:00 p.m.
AVW. 2328
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: Ph.D. Dissertation Defense
Name: Jing Xie
Advisory Committee:
Professor Shuvra S. Bhattacharyya, Chair/Advisor
Professor Rong Chen, Co-Chair/Co-Advisor
Professor Manoj Franklin
Professor Donald Yeung
Professor Ramani Duraiswami, Dean's Representative
Professor Shuvra S. Bhattacharyya, Chair/Advisor
Professor Rong Chen, Co-Chair/Co-Advisor
Professor Manoj Franklin
Professor Donald Yeung
Professor Ramani Duraiswami, Dean's Representative
Date/Time: Wednesday, August 9, 2023 at 3pm-5pm
Location: AVW 2328
Title: Methods and Tools for Real-Time Neural Image Processing
Abstract:
As a rapidly developing form of bioengineering technology, neuromodulation
systems 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 two
core 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 and
implementation 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.
systems 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 two
core 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 and
implementation 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.
