Neuromorphic Computing Seminar Report PDF | PPT

Neuromorphic Computing

Neuromorphic computing is a type of computer engineering in which computer components are designed after the human brain and nervous system systems. The word encompasses both the hardware and software aspects of computers. Neuromorphic computers are designed to provide the fastest computation speeds while avoiding the need for large devices and special structures. It's worth noting that contemporary supercomputers require megawatts of electricity, whereas the human brain uses only 20 watts, which academics are obsessed with recreating in computers.

To create artificial neural systems inspired by biological architecture, neuromorphic engineers depend on a variety of fields, including computer science, biology, mathematics, electronic engineering, and physics.

Potential Applications of Neuromorphic computing

• For running AI algorithms at the edge instead of in the cloud 
• driverless cars
• smart home devices
• natural language understanding
• data analytics
• process optimization
• real-time image processing for use in police cameras

Current Real-world examples of Neuromorphic Systems

1. IBM's TrueNorth chip
2. Intel's Loihi chips
3. The Tianjic chip. 
4. Intel's Pohoiki Beach computers
5. BrainScaleS from Heidelberg University

Neuromorphic Computing Seminar Report PDF and PPT

Neuromorphic Computing: Architectures, Models, and Applications (PDF for Seminar Report)

Introduction to Neuromorphic Computing: Insights and Challenges (Paper Presentation)

Neuromorphic Computing: From Materials to Systems Architecture (PDF)

Neuromorphic Computing: Concepts, actors, applications, market and future trends (Full Report)

Development of a neuromorphic computing system (PDF)

Neuromorphic Computing and Neural Networks in Hardware (PDF)

Neuromorphic Computing: a computer systems perspective (PPT)

Neuromorphic computing is a new field of technology that is currently in its early stages of development. The practical application of neuromorphic computer architectures has only recently been attempted. Recent advances in neuromorphic hardware have the potential to boost the efficiency of current neural networks, which are now powered by inefficient graphics processing units (GPU). A working human brain chip, on the other hand, is still a long way off.