- Neuromorphic Computing - Home
- Neuromorphic Computing - Introduction
- Neuromorphic Computing - Difference From Traditional Computing
- Neuromorphic Computing - History and Evolution
- Neuromorphic Computing - Types of Technologies
- Neuromorphic Computing - Architecture
- Neuromorphic Computing - Memristors
- Neuromorphic Computing - Synaptic Devices
- Neuromorphic Computing - Hardware Accelerators
- Neuromorphic Computing - Neuromorphic Chips
- Neuromorphic Computing - Analog Circuits
- Neuromorphic Algorithms and Programming
- Neuromorphic Computing - Spiking Neural Networks (SNNs)
- Neuromorphic Computing - Algorithms for SNNs
- Neuromorphic Computing - Programming Paradigms
- Applications of Neuromorphic Computing
- Neuromorphic Computing - Edge Computing
- Neuromorphic Computing - IoT
- Neuromorphic Computing - Robotics
- Neuromorphic Computing - Autonomous Systems
- Neuromorphic Computing - AI and ML
- Neuromorphic Computing - Cognitive Computing
- Neuromorphic Computing Resources
- Neuromorphic Computing - Useful Resources
- Neuromorphic Computing - Discussion
Neuromorphic Computing - Neuromorphic Chips
Neuromorphic Chips are specially designed chips for neuromorphic systems to exhibit the architecture and functionality of the human brain. These chips can perform complex tasks like real-time pattern recognition, sensory processing and adaptive learning with low power. In this section we will discuss a detailed overview on neuromorphic chips, it's component, features and examples.
Components of Neuromorphic Chips
Neuromorphic chips consist of three main components, they are:
- Artificial Neurons: These components simulate the behavior of biological neurons. It will process incoming signals and generate output spikes when certain thresholds are exceeded.
- Artificial Synapses: Neuromorphic chips include synapses that are connecting multiple neurons for transmission of signals. Learning process happen when these synapse adjusts their strengths.
- Plasticity Mechanisms: Just like synaptic plasticity in the brain, neuromorphic chips feature mechanisms that allow synapses to strengthen or weaken based on learning processes.
Features of Neuromorphic Chips
- Low Power Consumption: Neuromorphic chips are designed to operate with minimal energy usage.
- Parallelism: Neuromorphic chips can handle multiple computations simultaneously, same as the human brain processes many sensory inputs in parallel.
- Scalability: These chips can scale to include millions of neurons and synapses according to need.
- Event-Driven Processing: Neuromorphic chips are designed for event-driven computation, meaning that neurons process data only when triggered by external inputs.
- Adaptation and Learning: Neuromorphic chips can dynamically adjust their synaptic weights and neuronal thresholds according to new information.
Examples of Neuromorphic Chips
- Intel Loihi: A neuromorphic chip that features over 130,000 artificial neurons and 130 million synapses. Loihi supports on-chip learning and can handle complex tasks like sensory processing and object recognition.
- IBM TrueNorth: This is one of the earliest neuromorphic chip. IBM's TrueNorth contains one million neurons and 256 million synapses, designed to mimic the human brain's parallel processing capabilities while using very low power.
- BrainChip Akida: An advanced neuromorphic chip designed for edge computing applications. It excels in pattern recognition, real-time adaptation, and sensory data processing. It is used for tasks like autonomous driving and robotics.
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