The document discusses neuromorphic computing, emphasizing its inspirations from the human brain and its significance for modern computing, particularly in overcoming limitations of traditional architectures such as energy inefficiency and scalability. It reviews key features like spiking neural networks, event-driven architectures, and synaptic plasticity, and presents various neuromorphic hardware examples, including IBM's TrueNorth, Intel's Loihi, and Spinnaker. The document highlights the architecture and advantages of these systems in providing efficient and scalable processing solutions.

1. Neuromorphic Computing: The
Future of Intelligent Processing
Bridging Biology and Technology
[Your Name and Affiliation]

2. Introduction
• - Define neuromorphic computing.
• - Highlight its inspiration from the human
brain.
• - Briefly state its importance in modern
computing.

3. Why Neuromorphic Computing?
• - Challenges of traditional von Neumann
architectures:
• - Energy inefficiency
• - Limited scalability
• - Slow real-time performance
• - Need for brain-inspired systems.

4. Biological Inspiration
• - Overview of the human brain:
• - Neurons and synapses.
• - Spike-based communication.
• - Parallelism in the brain.

5. Key Features
• - Spiking Neural Networks (SNNs).
• - Event-driven architecture.
• - Learning mechanisms (e.g., synaptic
plasticity).
• - Energy efficiency.

6. Architecture of Neuromorphic
Systems
• - Neurons, synapses, and interconnections.
• - Differences from conventional architectures.
• - Overview of parallel and distributed
computing in neuromorphic systems.

7. Hardware Overview
• - Brief history of neuromorphic chips.
• - Examples:
• - IBM TrueNorth
• - Intel Loihi
• - SpiNNaker.

8. IBM TrueNorth
• - Description and features.
• - Number of neurons and synapses supported.
• - Key applications.

9. Intel Loihi
• - Description and architecture.
• - Focus on on-chip learning.
• - Power efficiency and scalability.

10. SpiNNaker
• - Architecture and design philosophy.
• - Scalability and biological simulation
applications.