6. Neuromorphic Computing and Hardware Accelerators
Neuromorphic computing seeks to replicate the brain's architecture, allowing for parallel information processing and energy-efficient AI systems. Key principles include spiking neural networks, brain-inspired architectures, and neuromorphic hardware accelerators such as IBM's TrueNorth and Intel's Loihi. The chapter discusses the advantages of neuromorphic systems, particularly in real-time processing and low power consumption, while also addressing the challenges of hardware limitations and software compatibility.
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What we have learnt
- Neuromorphic computing mimics the brain's architecture and processes information in parallel.
- Spiking neural networks and synaptic plasticity are fundamental to neuromorphic systems.
- Neuromorphic hardware accelerators improve energy efficiency and processing speed for AI applications.
Key Concepts
- -- Neuromorphic Computing
- An approach to computing that mimics the architecture and functioning of biological neural networks to achieve energy-efficient and scalable AI solutions.
- -- Spiking Neural Networks (SNNs)
- A type of neural network that uses discrete spikes for communication between neurons, resembling biological processes.
- -- SpikeTimingDependent Plasticity (STDP)
- A learning rule in neuromorphic systems that adjusts synaptic weights based on the timing of spikes, mimicking how the brain forms memories.
- -- Neuromorphic Hardware Accelerators
- Specialized chips designed to perform neuromorphic computing tasks efficiently, such as IBM's TrueNorth and Intel's Loihi.
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