Introduction

Neuromorphic Computing is an interdisciplinary field that seeks to model computational systems based on the architecture and functioning of the human brain. This approach aims to create more energy-efficient, adaptive, and intelligent computing systems. By mimicking biological neural networks, Neuromorphic Computing can revolutionize industries such as robotics, AI, cognitive computing, and hardware design. This course provides an in-depth understanding of the principles, technologies, and applications of Neuromorphic Computing, offering hands-on experience with state-of-the-art neuromorphic hardware and software tools.

Objectives

By the end of this course, participants will:

• Understand the fundamental principles and theories behind neuromorphic computing.
• Gain knowledge of the biological inspiration for neuromorphic systems and how it influences their design.
• Learn how to develop neuromorphic algorithms and applications for real-world use cases.
• Understand neuromorphic hardware, including systems like TrueNorth, Loihi, and SpiNNaker.
• Develop practical skills in designing and simulating neural networks with neuromorphic principles.
• Explore the challenges and future directions in neuromorphic hardware and AI acceleration.

Who Should Attend?

This course is suitable for:

• AI Researchers and Data Scientists interested in novel, brain-inspired computational methods.
• Hardware Engineers and Systems Designers focused on neuromorphic chips and AI hardware accelerators.
• Robotics Engineers interested in building intelligent systems with low energy consumption.
• Software Developers and Algorithm Engineers exploring neuromorphic algorithms for machine learning applications.
• Academics and PhD students in fields such as neuroscience, artificial intelligence, and electrical engineering.

Day 1: Introduction to Neuromorphic Computing

Morning Session: Overview of Neuromorphic Computing

• What is Neuromorphic Computing?
• The inspiration from neuroscience: How the brain functions and its relevance to computing.
• Basic components of a neuromorphic system: Neurons, synapses, and spiking neural networks (SNNs).
• The difference between traditional Von Neumann architecture and neuromorphic systems.
• Neuromorphic systems vs. AI hardware accelerators (e.g., GPUs, TPUs).

Afternoon Session: Key Neuromorphic Technologies

• Introduction to Spiking Neural Networks (SNNs): The core of neuromorphic computing.
• Synaptic plasticity and learning rules: Hebbian learning, STDP (Spike-Timing Dependent Plasticity).
• Hardware implementations: TrueNorth, Loihi, SpiNNaker, and Brain-Inspired Computing.
• Real-world examples: Neuromorphic systems in robotics, vision processing, and autonomous systems.
• Hands-on: Exploring basic spiking neural networks using software simulations.

Day 2: Biological Inspiration and Computational Models

Morning Session: Biological Neurons and Synapses

• Understanding biological neurons: Structure, firing patterns, and communication.
• The role of synapses in signal transmission.
• Concepts of thresholding, neuroplasticity, and signal integration in neural systems.
• Case study: How biological vision systems inspired neuromorphic vision processors.
• Hands-on: Building a simple spiking neuron model in Python or another simulation tool.

Afternoon Session: Computational Models of Neuromorphic Systems

• Leaky Integrate-and-Fire (LIF) model: A fundamental model of a spiking neuron.
• Hodgkin-Huxley model: Advanced biologically-inspired model of neuron activity.
• Neural coding: Rate coding, temporal coding, and population coding.
• Spike-timing dependent plasticity (STDP) and its role in neuromorphic learning.
• Hands-on: Simulating STDP learning and applying it to a simple neural network.

Day 3: Neuromorphic Hardware and Software

Morning Session: Hardware for Neuromorphic Computing

• Introduction to TrueNorth: IBM’s neuromorphic chip and its architecture.
• Loihi by Intel: Neuromorphic chip designed for spiking neural networks.
• SpiNNaker: A massively parallel processing system for simulating brain-like processes.
• Neuro-Inspired Hardware for edge computing and low-power AI applications.
• Comparing traditional computing hardware vs. neuromorphic hardware in terms of energy efficiency, parallelism, and adaptability.
• Hands-on: Exploring TrueNorth/Loihi simulation tools or using open-source platforms for spiking neural networks.

Afternoon Session: Software and Frameworks for Neuromorphic Computing

• Software frameworks for neuromorphic systems: NEST, Brian2, BindsNET, SpiNNaker software.
• Simulating spiking neural networks with BindsNET (Python-based library).
• Integrating neuromorphic systems with other AI frameworks such as TensorFlow, PyTorch for hybrid architectures.
• Optimizing SNNs for real-time processing.
• Hands-on: Writing a simple SNN using BindsNET and running it on a neuromorphic simulator.

Day 4: Applications of Neuromorphic Computing

Morning Session: Neuromorphic Vision and Robotics

• Neuromorphic systems in computer vision: Low-power, high-efficiency edge processing.
• Building neuromorphic vision sensors and their applications in autonomous vehicles, robotics, and drones.
• Neuromorphic robots: How neuromorphic systems enhance robot adaptability and decision-making.
• Use case: Neuromorphic systems in image recognition and object detection.
• Hands-on: Exploring neuromorphic vision systems for real-time processing.

Afternoon Session: Autonomous Systems and AI Acceleration

• Neuromorphic robotics: Applications in autonomous decision-making and motion planning.
• AI acceleration: How neuromorphic computing improves speed and energy efficiency in machine learning tasks.
• Case study: Edge computing and IoT solutions using neuromorphic systems.
• Collaborative decision-making with multiple neuromorphic agents.
• Hands-on: Developing a robotic control system using neuromorphic principles.

Day 5: Challenges, Future Trends, and Hands-on Projects

Morning Session: Challenges in Neuromorphic Computing

• Scalability and hardware limitations in neuromorphic systems.
• Balancing energy efficiency and computational power.
• Overcoming communication bottlenecks and managing large-scale neuromorphic systems.
• Integration of neuromorphic systems with traditional computing architectures.
• The challenge of real-time performance and latency in practical applications.

Afternoon Session: Future Directions in Neuromorphic Computing

• The future of brain-inspired computing: Advances in quantum neuromorphics and DNA computing.
• The role of neuromorphic computing in AI ethics, privacy, and security.
• Neuromorphic computing for cognitive robotics, smart cities, and AI-driven healthcare.
• Group project: Developing a neuromorphic solution to a specific problem (e.g., autonomous driving, real-time monitoring system).
• Final review and Q&A session.

Materials and Tools:

• Software: Python-based neuromorphic simulation tools such as BindsNET, NEST, Brian2, and SpiNNaker.
• Neuromorphic hardware platforms: If available, simulation tools for TrueNorth, Loihi, and SpiNNaker.
• Datasets: Sample data for vision processing, robotics, and autonomous systems.

Post-Course Support:

• Access to course materials, recorded lectures, and simulation code examples.
• Ongoing support through a discussion forum for course-related queries and project feedback.
• Resources for continuing education in neuromorphic computing and related fields like cognitive computing and neuromorphic AI.