Neuromorphic Computing
Many technology and engineering concepts that exist today are modeled after the human body or nature. For example, Swiss electrical engineer George de Mestral developed Velcro on the basis of burrs found on his clothes and his dog after hunting. Japanese engineer Eiji Nakatsu was able to reduce train noise by modeling the bullet train after the beak of kingfisher birds.听
Then there is the example of the tardigrade, commonly referred to as a 鈥渨ater bear鈥, that has the remarkable ability to survive for numerous years in extreme conditions. These eight-legged microanimals inhabit aquatic environments. When these microanimals are removed from water, even after they dry out for as long as 100 years, they have the ability to come back to life by self-rehydrating. They survive this hibernation cycle by coating their DNA and proteins in a protective sugar called trehalose, which moves into the cells and replaces lost water. Biotechnology companies have studied these 鈥渨ater bears鈥 and adapted this process to protect live vaccines by coating the vaccine in a sugar film that can be stored at room temperature. This allows for vaccines to be shipped to various locations without the need of refrigeration. It also makes supplying vaccines to third world countries more cost-effective and easier to transport without the need of bulky freezer containers.听听
听 Another example of an engineering concept modeled after nature is neuromorphic computing.听 This method of computer engineering is directly inspired by the human brain. The human brain, as an organic model, remains the pinnacle of efficiency and power in processor design, serving as a benchmark for advancing computational capabilities. The systems in a neuromorphic computing model mimic the parallel processing of neurons and synapses which is far more efficient than traditional computers that use the von Neumann architecture (which separates memory and computing through binary code). Binary, utilizing only two digits (0 and 1), serves as the underlying language enabling traditional computers to process data, perform calculations, store information, and communicate with other devices. Compared to the human brain that operates in parallel, binary code tends to be linear and serial in function.听
Neuromorphic computers process and store data together on each individual neuron, while von Neumann computers have separate areas for each. This is called parallel processing, which allows many tasks to occur simultaneously. When a neuromorphic computer 鈥渢hinks,鈥 it can make different connections that layer on top of each other, using less storage for each individual piece of data.听听听
Currently, these computers are only available to experts at universities, billion-dollar companies and government-funded research labs. Even with the assistance of artificial intelligence, machine learning and computer science backgrounds, operating a neuromorphic computer requires extensive knowledge in different subjects including neuroscience and physics.听听
While working on a neuromorphic computer, researchers at the Australian Institute of Physics used silver wires at one thousandth of a human hair that randomly arranged themselves in a form like the neural network in humans. Not to be confused with artificial intelligence, the silver wires display 鈥渂rain-like鈥 behaviors in response to electrical signals. External electrical signals cause changes in how electricity is transmitted at the points where nanowires intersect, all of which mirror biological synapses.听听
The researchers also discovered that these networks respond and adapt quickly to changing signals, proving to be very useful in artificial intelligence modeling and computing. An important distinction is that artificial intelligence learns through 鈥渂atch-based鈥 process learning, which requires access to high amounts of memory and requires that the system iterates and trains itself multiple times to learn. A key quality of neuromorphic computing is that these human-like networks process data continuously and only needs to review the data once to be stored.听
Neuromorphic computers are modeled after the neocortex in the brain, where sensory perception, language and spatial reasoning occur. The neocortex consists of neurons and synapses that instantaneously carry information from the brain to the rest of the body. This is the process that tells your hand to move quickly if you touch a hot stove. This process is so fast and efficient that it is nearly impossible to replicate. Neuromorphic computers attempt to mirror this process by forming 鈥渟piking neural networks.鈥 In an article published by Built-In about neuromorphic computing, they state, 鈥淎 spiking neural network is the hardware version of an artificial neural network which is a series of algorithms run on a regular computer that mimics the logic of how a human brain thinks.鈥 Spiking neurons are essentially storage units that hold data similar to how biological neurons hold information. These neurons are connected through artificial synaptic networks that transfer electrical signals back and forth. Spiking neural networks only compute in response to spikes, which means only a few of a system鈥檚 neurons use power while the rest stay idle. These spiking neural networks have proven to be more energy efficient than quantum computers and von Neumann computers.听听听
Neuromorphic computing stands at the forefront of computer engineering, drawing inspiration from the remarkable efficiency and adaptability of the human brain. By mimicking the parallel processing capabilities of neurons and synapses, these systems promise to revolutionize computing power.听听听
The adaptability of neuromorphic computing holds potential for addressing challenges in various domains, from online artificial intelligence to energy-efficient computing. By leveraging spiking neural networks and artificial synaptic networks, these systems offer a pathway toward faster processing speeds, enhanced pattern recognition capabilities, and more efficient learning mechanisms, as compared to traditional computing architectures.听听
As we continue to explore the possibilities of neuromorphic computing based on nature's designs, we stand poised to unlock new frontiers in technology, driving innovation and advancing our understanding of intelligent systems.听听
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