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- Physics-Based Encoding for Spiking Neural Networks
Physics-Based Encoding for Spiking Neural Networks
Supervisors
Dr Nimrod Kruger & Prof Paul Hurley
Description
Efficient encoding of spike-based time-surfaces in neuromorphic computing remains an open challenge. While understanding biological neural encoding may eventually shed light on potential schemes, these are unlikely to provide compatibility to digital systems or standardized interfaces.
This project aims to explore the use of mathematical primitives and spike-based neural operators to define interfaces between spike-to-spike or spike-to-digital communication. These primitives and operators can offer interoperability by developers, maintaining the unique benefits of spike-based computing without compromising on compatibility and standardization.
Outcomes
- Literature survey – state-of-the-art interfaces of spike-to-digital and spike-to-spike modalities.
- Formulate spiking neural networks operators and/or mathematical primitives.
- Functional and comparative testing of these elements in context of transfer efficiency, signal fidelity, and robustness.
- Build a model interface for a set of tasks (robot arm control, navigation, user interface, etc.)
Eligibility Criteria
Background in Mathematics, Computer Science, or Mechanical / Electrical Engineering.
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- Task-Driven Model Evaluation in Large-Scale Spiking Neural Networks
- A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks
- Event Based Wavefront Sensing Modalities
- Physics-Based Encoding for Spiking Neural Networks
- Neuromorphic Computational Imaging
- Defining Performance Metrics for Closed Loop Event Based Imaging Systems
- A Neuromorphic Framework for Event-Based DNNs using Minifloats
- A RISC-V instruction set architecture (ISA) extensions for neuromorphic computing using minifloats
- Astrometry with Event-based Vision Sensors
- Automatic Evaluation of Bushfire Risk via Acoustic Scene Analysis
- Bio-inspired Sensors for Space Situational Awareness
- Building a Neuromorphic Auditory Pathway for Sensing the Surrounding Environment
- Cold Start Astrometry for High-Precision Airspace and Space Objects Tracking with Neuromorphic Cameras
- Control Systems Inspired by Insect Central Pattern Generators that can Adapt to Dynamic Environments.
- Design of Neuromorphic Spiking Neural Networks for Real-Time Processing
- Enhanced Maritime Situational Awareness with Neuromorphic Cameras
- Environmental Situational Awareness using Neuromorphic Vision Sensors and IMU-based SLAM
- Fault Tolerant Distributed Swarm Intelligence using Neuromorphic Computing and Local Learning Principles
- Honey Bee Waggle Dance Detection via Neuromorphic Engineering
- Integrated Circuit Design for Event-based Vision Sensors
- Low-Power Acoustic Ecological Monitoring in Remote Areas using Machine Learning and Neuromorphic Engineering
- Neuromorphic Computing in Extreme Environments
- Neuromorphic Cyber Security at the Edge
- Neuromorphic Engineering for Acoustic Aerial Drone Detection in Visually Obscured Environments
- Machine Learning-Based Tool for Therapists to Monitor Speech Progress in Late Talkers
- Machine Learning for Automated Child Reading Assessment and Intervention
- Underwater Acoustic Drone Detection via Neuromorphic Models of Marine Mammal Audition
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