**2.2 Neuromorphic computing architecture**

Neuromorphic computing is a method of computer engineering in which elements of a computer are modelled after systems in the human brain and nervous system. The phrase describes the creation of both software and hardware components in computers. To construct artificial neural systems that are inspired by biological architecture, neuromorphic engineers depend on a variety of fields, including computer science, biology, mathematics, electronic engineering, and physics.

Researchers have investigated neuromorphic computing throughout the years to create various types of machines that think and learn like humans. Studies have been done to imitate how the human brain learns and computes by using computer hardware in the form of an artificial neural network to simulate human learning abilities.

*Neuromorphic Computing between Reality and Future Needs DOI: http://dx.doi.org/10.5772/intechopen.110097*

#### **Figure 1.**

 *Three interconnected neurons. A presynaptic neuron transmits the signal toward a synapse, whereas a postsynaptic neuron transmits the signal away from the synapse [ 1 ].* 

The anatomy of the human brain, which has over one billion neurons and trillions of synapses, is extremely intricate. As shown in **Figure 1** , neurons are made up of a cell body, an axon that generates neural signal impulses, and a dendrite that receives signals from other neurons. A synapse is a physical component that enables one neuron to transmit an electrical signal to an additional neuron.

 As seen in **Figure 2b** , the neuromorphic hardware typically comprises of neurons and synapses to imitate the human brain's neural network. Each neuron functions as a data processing centre in the neuromorphic hardware, and neurons are linked together in parallel by synapses to convey data [ 2 – 4 ]. The single signal bus present in the neuromorphic hardware does not result in a von Neumann bottleneck. Instead of using regular CMOS devices, artificial synaptic devices that reflect the properties of bio-synapses must be developed in order to execute this in practical design. The block diagrams for the traditional von Neumann architecture and the emerging neuromorphic design are shown in **Figure 2** .

 Significant improvements have recently been made in the field of neuromorphic computing. Three major steps are used to categorise the most recent improvement [ 6 – 8 ]. A GPU-Centric system, which is primarily used for learning and supports artificial intelligence by using a graphics processing unit (GPU), is the initial phase. An ASIC-Centric system, which is now the next stage, is a hot topic of research. An effective and low-power application-specific integrated circuit (ASIC) for machine learning is anticipated to be created by this trend. As a result, numerous semiconductor firms are creating ASIC chips [ 9 – 12 ]. However, it is anticipated that neuromorphic computing will be developed 1 day into neuromorphic hardware, enabling ultra-low power and ultra-high speed computation to support for general-purpose artificial intelligence.

 **Figure 2.**

 *Block diagram of computing systems: (a) von Neumann architecture; (b) neuromorphic architecture [ 5 ].* 

The neuromorphic-centric hardware must be able to handle massive amounts of data in parallel while using incredibly little power. Furthermore, compared to current technology that uses ordinary CMOS components, the neuromorphic semiconductor chip demands a quicker rate of computing. This suggests that creating an emerging synaptic device is essential to the development of neuromorphic-centric hardware.
