Neuromorphic computing is groundbreaking way of creating equipment and software that imitates human brain. It provides an efficient and long lasting alternative to traditional computing in creating most advanced AI technology.
In last few decades we have seen an ever growing improvement in capabilities of computers. From humble calculator to advanced devices that harness potential in AI as well as machine learning in order to forecast any kind of situation with astonishing accuracy and create media (e.g. ChatGPT) and development of computing tells us many things about our modern world and how we came to where we are today.
Yet modern computer is getting to limit of their capabilities. Due to rapid growth of AI current methods of computing have proved inefficient slow and energy intensive. quantity of energy needed to make AI technology ubiquitous is too much and is expensive in terms of environmental impact.
Neuromorphic computing is an innovative method that attempts to overcome shortcomings of existing machines. Although concept is not brand new (it has roots in early 1980s when scientists Misha Mahowald as well as Carver Mead developed first cochlea silicon retina and first silicon neuron and synapses which established concept of neuromorphic computing model) It is gaining traction rapidly.
It is an attempt to make comprehensive overhaul of method by which software and hardware are designed and constructed. aim is to develop computers that mimic neurons and brains of humans.
How Does Neuromorphic Computing Work?
In order to understand how neuromorphic computing functions we must first to comprehend how our brain functions.
Simply put functions of processing and memory occur through neurons and synapses. Synapses and neurons transmit messages to and through brain with near instantaneous velocity and virtually no energy use. This is why how for example we are able to quickly distinguish difference between dog and cat or slow down our vehicle when there is person walking across an intersection of roadway.
The secret to our remarkable ability to deal with complexity of world is fact that processing and memory processes are carried out at same time. This is fundamental difference opposed to traditional computers that have distinct processing and memory units.
Although this approach is efficient in complicated tasks for example predicting if its going to rain on next day however its not ideal for tasks that we are able to handle like visual recognition reasoning or even walking.
The emergence of neuralmorphic computing will mean that all of these functions will be accomplished using single chip built to run what are referred to as Spiking neural networks (SNNs) (SNNs) which are name of kind of artificial neural network that is made up of synapses and spiking neurons. Read our article for more information on nature of neural networks and more in depth.
SNNs differ from traditional neural networks due to requirement to transfer information from memories (RAM) as well as processing units (CPU and GPU). They also operate on an event basis which means that they respond to events when they happen (based upon spiking mechanism which relies on thresholds for activating neurons).
This means that SNNs will increase processing speed and cut down on amount of energy associated with. These improvements in latency could have significant impact especially in applications where computer systems rely on live sensor information like cameras used in autonomous vehicles.
Applications of Neuromorphic Computing
Neuromorphic computing has potential to create an innovation with profound implications for industry. If claims of unprecedented efficiency increases are made widely used it could become major game changer for many applications by speeding up development process and implementing.
Below you will find some of applications that could benefit from neuromorphic computation.
Machine learning and Artificial Intelligence
AI is among most transformative and disruptive technology weve created. applications of AI are limitless but like weve already stated our current technology is insufficiently equipped to take advantage of full power of AI.
Neuromorphic computing is an exciting computing strategy that has been proven suitable for issues that demand most efficient use of energy speedy processing and adaptability like pattern recognition or sensor processing. In particular neuromorphic method of learning is being used to assist in recognising patterns in natural speech as well as to aid in recognition of faces for to identify facial features.
Autonomous cars
An excellent example of how neuroscience based computing can transform applications which require sensor processing is autonomous vehicles. makers of autonomous cars employ several cameras as well as sensors to collect images from surroundings to ensure that their autonomous vehicles recognize objects that are in lane markings on road and traffic signal signs enabling them to navigate safely.
Because of their efficiency and speed of operation Neuromorphic computers are able to enhance autonomous vehicle ability to navigate which allows for faster and better decision making that is crucial to reduce possible accidents. It also allows for reduction of use of energy. It will ultimately lead to an extended battery lifespan for cars.
If youre curious about technology behind autonomous vehicles then heres DataCamp project that aims to identify traffic signals using deep learning to help to get your feet wet in this field.
Robotics
Similar to autonomic vehicles Neuromorphic computing is seen as an essential technology that can bring robotics to different level in its development. Robots that are powered by neurons may be better equipped to tackle traditional issues that are being faced by industry that include need to facilitate instantaneous learning and making decisions for real world scenarios and incorporating batterys limitations.
Are you curious about how machines work with environment? Learn more about machine perception as well as intelligent sensors.
Edge computing
Advanced AI technology relies in distant cloud servers to perform their work. This is risk when it comes to applications that require quick response like self driving vehicles as well as those running on devices with very limited resources. Edge computing is method of achieving this by providing AI capabilities directly into device.
The advancements in neuromorphic computing could change way we use field of edge computing. Due to lower energy consumption of these devices phones and other types of smart wearables that usually have limited battery lives can perform numerous new tasks were previously requiring large amount of power.
Advantages of Neuromorphic Computing
We will go over some of main motives behind why PwC recently declared neuromorphic computing among eight emerging technologies that are essential currently.
Efficiency in energy use
Neuromorphic computing might serve as basis to new level of intelligent computing. Innovative software and hardware components could result in greater efficiency on both processing data and energy consumption. improvements in technology will allow companies to cut operational costs as they develop more efficient and precise AI software and hopefully lessen carbon footprint of their operations.
Parallel processing
Parallel processing is process of process of computing and splitting it into smaller parts that are distributed across multiple servers. They then finish tasks in faster and more efficient manner. features of SNNs allow them to be particularly effective for applications that require use of parallel processing such as patterns recognition.
Flexible learning in real time and adaptability
Neuromorphic computers that are based on SNN excel at rapid learning in real time and flexibility. These flexibility and flexability could be crucial for AI applications that demand constant learning and rapid making of decisions such as robots or autonomous cars.
Challenges and Limitations
The benefits of neuromorphic technology are evident however technology is still in its early stages and is not yet ready to be accepted as standard. Below are few problems to come.
Inadequate Standards
There is an increasing number of projects involving neuromorphs on market most of them are in well funded research labs or universities. It is clear that research isnt quite ready to be put into marketplace. It is still lacking of software and hardware standards that make scaling almost feasible.
Access to internet is not always easy.
The absence of standardization implies that we arent able to find terms to describe and explain various components and functions of neuromorphic computers. This is reason reason why only tiny percentage of specialists around world are acquainted of neuromorphic computing. It is as Andreea Danielescu an associate director of Accenture Lab declares:
For those with deep AI and machine learning background. It is complex process that requires knowledge of different areas such as neuroscience computer science and Physics.
Andreea Danielescu Associate Director at Accenture Lab
Integration into existing systems
In end even if claims of technology are real process of establishing its presence to market and through ecosystem of technology will require some time and effort. Neuromorphic computing advocates 180 degree change in design of computing. But majority of deep learning applications available depend on neural networks that depend on conventional hardware. This could cause major issues when it comes to integrating these technologies into existing computing infrastructures.
The Future of Neuromorphic Computing
Without paradigm shift well not have resources to fuel an AI revolution. Daniel Bron tech consultant provides towards this direction pointing out possibility of neuromorphic computing.
AI will not advance to level it requires to be even with technology weve got. Neuromorphic computing is lot better in processing AI. Do you think its necessary? Im not sure if its required but its not yet. However it is definitely more effective.
Daniel Bron Tech Consultant
Everybody in AI business is aware of shortcomings of computers today and are looking for new techniques such as quantum computing and neuromorphic computing to bring expenses down and improve profits. This is why worldwide neuroscience based computing market is predicted to increase by 20% over coming five years.
In future in near future we could see creation of hybrid computing devices with new neuromorphic chips that can improve performance of current AI applications. Long term it is possible that combination of quantum and neuromorphic computing could lead us into an entirely new world of computing.