Neuromorphic computing is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system. The term refers to the design of both hardware and software computing elements. Neuromorphic computing is sometimes referred to as neuromorphic engineering.
Neuromorphic engineers draw from several disciplines — including computer science, biology, mathematics, electronic engineering and physics — to create bio-inspired computer systems and hardware. Of the brain’s biological structures, neuromorphic architectures are most often modelled after neurons and synapses. This is because neuroscientists consider neurons the fundamental units of the brain.
Neurons use chemical and electronic impulses to send information between different regions of the brain and the rest of the nervous system. Neurons use synapses to connect to one another. Neurons and synapses are far more versatile, adaptable and energy-efficient information processors than traditional computer systems.
Neuromorphic computing is an emerging field of science with no real-world applications yet. Various groups have research underway, including universities; the U.S. military; and technology companies, such as Intel Labs and IBM.
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Neuromorphic technology is expected to be used in the following ways:
Some experts predict that neuromorphic processors could provide a way around the limits of Moore’s Law.
The effort to produce artificial general intelligence (AGI) also is driving neuromorphic research. AGI refers to an AI computer that understands and learns like a human. By replicating the human brain and nervous system, AGI could produce an artificial brain with the same powers of cognition as a biological one. Such a brain could provide insights into cognition and answer questions about consciousness.
Neuromorphic computing uses hardware based on the structures, processes and capacities of neurons and synapses in biological brains. The most common form of neuromorphic hardware is the spiking neural network (SNN). In this hardware, nodes — or spiking neurons — process and hold data like biological neurons.
Artificial synaptic devices connect spiking neurons. These devices use analog circuitry to transfer electrical signals that mimic brain signals. Instead of encoding data through a binary system like most standard computers, spiking neurons measure and encode the discrete analog signal changes themselves.
The high-performance computing architecture and functionality used in neuromorphic computers is different from the standard computer hardware of most modern computers, which are also known as von Neumann computers.
Von Neumann computers have the following characteristics:
Neuromorphic computers have the following characteristics:
Many experts believe neuromorphic computing has the potential to revolutionize the algorithmic power, efficiency and capabilities of AI as well as reveal insights into cognition. However, neuromorphic computing is still in early stages of development, and it faces several challenges:
Despite challenges, neuromorphic computing is still a highly funded field. Experts predict neuromorphic computers will be used to run AI algorithms at the edge instead of in the cloud because of their smaller size and low power consumption. Much like a human, AI infrastructure running on neuromorphic hardware would be capable of adapting to its environment, remembering what’s necessary and accessing external sources, like the cloud, for more information when necessary.
Other potential applications of this technology include the following:
Neuromorphic computing research tends to take either a computational approach, focusing on improved efficiency and processing, or a neuroscience approach, as a means of learning about the human brain. Both approaches generate knowledge that is required to advance AI.
AGI refers to AI that exhibits intelligence equal to that of humans. Reaching AGI is the goal of AI research. Though machines have not, and may never, reach a human level of intelligence, neuromorphic computing offers the potential for progress toward that goal.
For example, the Human Brain Project is an AGI project that uses the SpiNNaker and BrainScaleS neuromorphic supercomputers to perform sufficient neurobiological functions attempting to produce consciousness.
Some of the criteria for determining whether a machine has achieved AGI is whether the machine has the following capabilities:
Sometimes the capacity for imagination, subjective experience and self-awareness are included. Other proposed methods of confirming AGI include the Turing Test, which states that a machine is sentient if an observer cannot tell it apart from a human. The Robot College Student Test is another test, in which a machine enrolls in classes and obtains a degree like a human student.
There are debates about the ethics and legal issues surrounding the handling of a sentient machine. Some argue that sentient machines should be treated as a nonhuman animal in the eyes of the law. Others argue a sentient machine should be treated as a person and protected by the same laws as human beings. Many AI developers follow an AI code of ethics that provides guiding principles for the development of AI.
Scientists have been attempting to create machines capable of human cognition for decades. Work in this area has been closely tied to advances in mathematics and neuroscience. Early breakthroughs include the following:
1936. Mathematician and computer scientist Alan Turing created a mathematical proof that a computer could perform any mathematical computation if it was provided in the form of an algorithm.
1948. Turing wrote “Intelligent Machinery,” a paper that described a cognitive modeling machine based on human neurons.
1949. Canadian psychologist Donald Hebb made a breakthrough in neuroscience by theorizing a correlation between synaptic plasticity and learning. Hebb is commonly called the “father of neuropsychology.”
1950. Turing developed the Turing Test, which is still considered the standard test for AGI.
1958. Building on these theories, the U.S. Navy created the perceptron with the intention of using it for image recognition. However, the technology’s imitation of biological neural networks was based on limited knowledge of the brain’s workings, and it failed to deliver the intended functionality. Nevertheless, the perceptron is considered the predecessor to neuromorphic computing.
1980s. Neuromorphic computing as it’s known today was first proposed by Caltech professor Carver Mead. Mead created the first analog silicon retina and cochlea in 1981, which foreshadowed a new type of physical computation inspired by the neural paradigm. Mead proposed that computers could do everything the human nervous system does if there was a complete understanding of how the nervous system worked.
2013. Henry Markram launched the HBP with the goal of creating an artificial human brain. HBP incorporated state of the art neuromorphic computers and has a 10-year horizon in which to better understand the human brain and apply this knowledge to medicine and technology. Over 500 scientists and 140 universities across Europe are working on the project.
2014. IBM developed the TrueNorth neuromorphic chip. The chip is used in visual object recognition and has lower power consumption than traditional von Neumann hardware.
2018. Intel developed the Loihi neuromorphic chip that has had applications in robotics as well as gesture and smell recognition.
Recent progress in neuromorphic research is attributed in part to the widespread and increasing use of AI, machine learning, neural networks and deep neural network architectures in consumer and enterprise technology. It can also be attributed to the perceived end of Moore’s law among many IT experts.
Moore’s Law states that the number of transistors that can be placed on a microchip will double every two years, with the cost staying the same. However, experts forecast that the end of Moore’s Law is imminent. Given that, neuromorphic computing’s promise to circumvent traditional architectures and achieve new levels of efficiency has drawn attention from chip manufacturers.
Recent developments in neuromorphic computing systems have focused on new hardware, such as microcombs. Microcombs are neuromorphic devices that generate or measure extremely precise frequencies of color. According to a neuromorphic research effort at Swinburne University of Technology, neuromorphic processors using microcombs can achieve 10 trillion operations per second. Neuromorphic processors using microcombs could detect light from distant planets and potentially diagnose diseases at early stages by analyzing the contents of exhaled breath.
Because of neuromorphic computing’s promise to improve efficiency, it has gained attention from major chip manufacturers, such as IBM and Intel, as well as the United States military. Developments in neuromorphic technology could improve the learning capabilities of state-of-the-art autonomous devices, such as driverless cars and drones.
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