**2.3 Neuromorphic computing goals**

Everything appears to be becoming "smarter" these days. Artificial intelligence (AI) is being used by an increasing number of goods and services, from industrial machinery to residential appliances, to comprehend user requests, analyse data, and spot patterns. It's simple to understand why AI-powered goods are so well-liked. Instead of using buttons or touchscreens, smart interfaces enable voice and gesture control of devices. It's a lot more instinctive to use a device in this way. Additionally, AI has the potential to make products more autonomous, freeing us from laborious or repetitive tasks. Smart items can also enable ongoing optimization and data analysis, which can be used to monitor our health and send us alerts, as well as to anticipate when a piece of equipment needs to be serviced or replaced. The popularity of smart devices today is increasing demand for increasingly complex AI-powered experiences. And we are beginning to push the capabilities of the hardware that is now available. Today's smart devices actually use a lot of processing that is done remotely in the cloud or a data center, where there is sufficient computer power to conduct the required algorithms. This means that a network connection is necessary, and when data is transmitted back and forth, this can also cause latency to grow. When sending particular types of data to the cloud, there are additional real and perceived data privacy factors to take into account.

These factors suggest benefits of increasing the amount of smart computing housed within the device itself. Since the processing is carried out in edge network devices rather than a centralised cloud, this is referred to as "edge AI." However, because many edge processors are mobile, they frequently rely on batteries for power. How can we run low-power edge devices with power-hungry AI algorithms? To do that, we are going to need to reevaluate how AI hardware is created.

This is the goal of neuromorphic computing. The information processing methods used by a biological brain are the foundation of neuromorphic computing design. Consider the 80–100 billion neurons found in the average human brain, each of which functions incredibly effectively and asynchronously to offer huge parallel processing. We are able to be so intelligent without constantly ingesting enormous quantities of energy because to this power and efficiency combination. Spiking neural networks are one of the most effective ways to simulate how a real neuron fires or "spikes" to relay a signal before going back to being silent. The end result is a system that uses much less power than artificial neural networks, which are currently employed in the majority of AI systems. Additionally, such efficiency makes it possible for considerably more AI processing to be done on smaller, low-power devices at the network edge.
