![Neuromorphic Computing: The
Future of Intelligent Processing
Bridging Biology and Technology
[Your Name and Affiliation]](https://image.slidesharecdn.com/neuromorphiccomputingpresentation-241118035954-fe76eb9b/85/Neuromorphic_Computing_Presentation-pptx-1-320.jpg)
Neuromorphic_Computing_Presentation.pptx
- 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.