A new brain-inspired approach to computer architecture
Machine learning has seen tremendous advances in recent years, enabling transformative technologies like autonomous vehicles, intelligent assistants, and more.
However, current machine learning systems rely on conventional computing architectures like CPUs and GPUs that are reaching their limits in terms of computational efficiency and scalability.
Neuromorphic computing provides a potential solution that takes inspiration from the brain’s neural networks to achieve huge gains in efficiency and capabilities. In this post, we’ll dive deep into the world of neuromorphic computing and explore why it holds great promise for taking machine learning to the next level.
Neuromorphic chips represent a radical departure from traditional digital logic circuits.
They are built from analog circuits that mimic neuro-biological architectures to process information more like biological brains.
Let’s look at some of their key features and advantages:
Neuromorphic processors contain neural components like neurons, axons, and synapses that are modeled on nervous system structures and connectivity patterns. This enables efficient information encoding and processing.
The analog circuits in neuromorphic chips are adaptive and can reconfigure themselves during operation by adjusting synaptic weights dynamically like real neurons. This allows real-time learning and adaptation.
Extremely low power
Neuromorphic systems are orders of magnitude more energy-efficient than traditional computing, enabling a range of new edge applications.
For example, a neuromorphic chip by IBM called TrueNorth operates with just 20mW of power.