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Neuromorphic Computing
Neuromorphic systems aim to emulate the fundamental principles of information processing in the brain and to create AI systems that are more efficient, adaptable, and robust than traditional computing architectures. This approach to AI design aims to create computer systems that can process information in ways similar to how the brain works, using networks of artificial neurons and synapses that can adapt and learn from experience. By emulating the brain's fundamental information-processing strategies, neuromorphic systems have the potential to solve complex real-world problems that traditional AI approaches may struggle with. Neuromorphic systems accomplish this by emulating not only the neuron structure of the brain but also the function of synapses through memristors. In a crossbar architecture, the neurons are arranged in a grid, and the synapses are implemented as the intersection of rows and columns in the grid. In addition, neuromorphic systems can be more power-efficient than traditional computing systems, as they are designed to use hardware architectures that more closely resemble the brain's low-power, parallel processing capabilities.
Analysis of Parasitics on CMOS-based Memristor Crossbar Array for Neuromorphic Systems
Crossbar arrays are commonly used in neuromorphic systems as a hardware architecture for implementing artificial neural networks. In these systems, the crossbar array serves as the physical substrate for the network's synapses, where each cross-point represents a synapse that can store a weight or conductance value. A crossbar array is a type of hardware architecture that consists of an array of nanoscale wires, where each wire intersects with a set of perpendicular nanoscale wires.
However, the nanoscale wires in a crossbar array have finite resistance and capacitance, and these parasitic effects can cause various problems. For example, the resistance of the wires can lead to voltage drops across the array, which can affect the accuracy of the stored values. The parasitic capacitance of the wires can also cause crosstalk between adjacent memory cells, leading to interference and noise that can degrade system performance.
3D Memristive Crossbar Array for Neuromorphic Integrated Circuits
Three-dimensional (3D) crossbars, when compared to conventional two-dimensional (2D) crossbars in neuromorphic systems, have several advantages. 3D crossbars can provide higher device density than 2D crossbars, leading to higher system performance and efficiency. By stacking multiple layers of crossbars on top of each other, 3D crossbars can significantly increase the number of synapses that can be packed into a given area, enabling the implementation of larger and more complex neural networks. This increased density also reduces the need for long interconnects between the synapses, which can improve the system's energy efficiency and reduce noise.
Another advantage of 3D crossbars is that they can support multi-level synapses, improving the system's accuracy and dynamic range. Multi-level synapses allow for storing multiple weights per synapse, rather than just a binary on/off state. This enables the system to represent more nuanced and subtle relationships between inputs and outputs, leading to better performance
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