Upon hearing the word “Computing”; our brain automatically forms the picture of a high-tech computer with hefty codes and well-lit screens full of algorithms. But, fortunately, this stereotypical picture is no more valid.
The scene might have been true had we been living in the 1990s; but today, computer automation has got a new face. We can now associate computing with other niches which could sound bizarre to a researcher 20 years back. The mind-blowing innovation we are talking about is – Neuromorphic Computing.
1. Neuromorphic Computing
Before getting into the discussion, we must get an idea of what this term stands for. For a newbie who has no clue about tech and computations, neuromorphic computing simply means the sector of computer science wherein we try to construct computer systems based on the workings and architecture of the human brain.
In other words, computers having working models similar to that of the human mind are being built that can process with the same or even better efficiency than the natural brain. We can consider neuromorphic computing as an umbrella category as it calls for several different science niches for the success of such a project.
Thus, Neuromorphic computing is also associated with several sub-branches like software engineering, AI development, Computer-Human Interaction(CHI), etc.
2. Pieces of the Jigsaw
Now that we have a fair idea of the term, let’s get through with the basics of its structure. Overall, such systems have mainly 5 components namely-
2.1. Humanoid Brain
The very basic requirement for the working of any technology inspired by the human body is the body organ itself. In this case, the human brain plays a significant role in the construction and development of the systems.
We must keep in mind that we don’t always need an actual human brain to conduct our research; most of the time scientists use dummies or humanoid brains (machines imitating natural brain-behavior) for the purpose. Before being able to construct such sophisticated and advanced technology, one needs to be well-versed in the functioning and mechanisms of the brain down to the tiniest details.
Current advancements in the field of engineering as well as biological research have proven to be of great help in the project’s success.
2.2 Neural Networks
Neural networks are the term given to a group of neurons that are interconnected and work together to perform signal transfer. The neurons we are referring to are not only the natural ones running all across our body. In the computing world, a well-built network of artificial neurons is also a vital key for the working of the system.
Artificial neurons are also built to serve the same functions that natural ones do. The only difference lies in the fact that they carry data across the system instead of conveying motor signals and hormonal control messages. Artificial neural networks (ANNs) are built on various technologies such as feedforward, recurrent, or convolutional depending on the specific application.
2.3. Software Framework
The core of any IT-based innovation is its programming software. to be able to copy something as complex and intimidating as the human brain, one would need a high-level program that can cope with the tricky algorithms and knotty data transfer mechanisms involved in the process.
We have been successful in devising some software like NEST, SpiNNaker, and BindsNET. However, the future of such potential projects would require more advancements over time.
2.4. Adaptable Hardware
Once we have engineered the program, we require the apt hardware that can support the system as well as run the program effectively. Neuromorphic hardware mainly relies on technologies that can analyze digital circuits to mimic the behaviour of biological neurons and control their functioning according to the user’s needs.
Building such hardware is a challenging task as it requires expertise in various sectors of science. Currently, we have several chips like IBM’s TrueNorth, Intel’s Loihi, and BrainChip’s Akida which fulfill the needs.
2.5. Learning Mechanisms
New-age technologies and gadgets are all being developed with keeping in mind their self-sufficiency. Engineers are focusing on programming their machines to learn from their mistakes and also from user interaction.
Development of Neuromorphic computing and other such technologies can be accelerated if we can install machine learning features into its working model. It would be best if the machine could update its understanding of the human brain while talking to it.
3. Putting the Jigsaw Together
In the following paragraphs, we have briefed out the jobs and working of the various components of the computing network.
3.1 Stimulating Neurons
Neuromorphic computing systems use artificial neurons known as “Spiking Neurons” as their functional unit. Like the Brain, all the data is communicated through these neurons in the form of electrical pulses or spikes.
The striking difference between traditional computing and neuromorphic computing lies in their mechanism of program initiation. Unlike conventional computing, these systems run programs only when stimulated just like the human brain.
3.2 Network Formation
The spiking neurons do not form individual end-to-end connections. There exists a network of these neurons interconnected and relaying messages across the machine. The junctions provide the centers of data control wherein data can be directed, deleted or new ones can be added by the user.
One need not assume that only a selected function occurs at a time. Like the biological brain, computers also perform a spectrum of jobs simultaneously. They are equipped with logistics to control and monitor the parallelism without fail.
3.3 Learning and Memory
The job of the system doesn’t end at the processing part. It also needs to learn from the user interaction and upgrade its output quality accordingly. Neuromorphic computing systems are built to mimic the human brain in all capacities; even memorizing stuff.
The computing systems used widely memorize things that are specifically told to them; whereas the neuromorphic computers are designed to remember things on their own. They are built specifically to be able to filter out the important data and memorize it without the user’s command.
4. Need of the Hour
No technology would be built if it didn’t have any practical application. Let us dig into some of the fields wherein neuromorphic computation is playing a crucial role. We will also discuss other niches where we are prospecting its use in the coming years.
4.1 Artificial Intelligence (AI):
Neuromorphic computing can significantly enhance AI systems by providing solutions to many of the present problems through its parallel processing capabilities. It can be used for tasks such as machine learning and pattern recognition which can be a big leap in the software engineering sector.
Natural language processing is also a key aspect of considering this new-age system. Neuromorphic systems can not only handle large-scale neural networks but also process the acquired data in real-time, making them suitable for AI applications.
Neuromorphic computing is a big boost for the development of intelligent and adaptive robots. By mimicking the structure and function of the human brain, neuromorphic systems can work with sensory inputs, make humanoid decisions, and adapt to the user’s command. This makes them useful in areas such as navigation and object recognition.
4.3 Sensor Networks:
Neuromorphic computing can be applied to sensor networks that require real-time data processing and energy efficiency at the same time. We are aware that these systems can process and produce data intelligently, thus they have great potential in environmental monitoring and surveillance.
4.4 Neuromorphic Vision Systems:
Neuromorphic computing has shown promise in developing advanced vision systems inspired by the human visual system. These systems can perform tasks such as object recognition and motion detection with human-like efficacy.
We are focusing on developing the technology for being able to replace humans in tiresome jobs like vehicle surveillance and providing solutions to building low-cost augmented reality and robotics.
4.5 Brain-Machine Interfaces:
Neuromorphic computing can contribute generously to the development of brain-machine interfaces (BMIs). These interfaces are designed to establish direct communication between the brain and external devices which the user uses to control the brain’s activity. We can see these systems being used in the field of neurogenic prosthetics and autonomous organs in the future.
5. Challenges to Overcome
While neuromorphic computing holds great potential, there are several limitations and challenges in building neuromorphic computers. Here are some of the key limitations:
5.1 Hardware Complexity
Neuromorphic hardware designs are highly complex and require sophisticated circuitry and specially designed components to mimic the behaviour of natural neurons and synapses. Moreover, the hardware constructed needs to be at par with the fast processing being conducted inside it. Developing and manufacturing such hardware can be challenging, costly, and time-consuming.
All the research is conducted on a small-scale dummy model. Scaling up neuromorphic systems to handle large-scale neural networks and complex tasks is a big challenge for engineers as they will have to face a larger number of neurons and synapses.
Constructing the machine on a larger scale also comes with other issues such as increment in the power consumption and larger memory requirements. Achieving scalability without compromising performance and efficiency is still ongoing research.
5.3 Programming and Software Tools
Developing software tools and programming frameworks that are well-fitted for the program’s neuromorphic hardware is a challenge. Currently, there is a need for user-friendly, high-level programming interfaces and tools that facilitate the deployment of neural network models on the required platforms. We must also keep in mind the human nature of the project we are discussing.
5.4 Learning and Training
While neuromorphic systems can exhibit learning and plasticity; designing efficient learning algorithms for these systems is still an active area of research. Training large-scale neural networks on neuromorphic hardware to perform, memorize, and optimize according to the user’s command; is definitely a tedious task.
By “noise” we are referring to the variations in circuitry and device characteristics that can introduce unpredictable behaviours in the system. Even extreme environmental conditions can lead to alterations in the system’s efficiency and prescribed functioning.
5.6 Integration with Existing Systems
If you believe that building such a futuristic masterpiece was the tougher task, you are highly mistaken. Engineers are challenged with integrating neuromorphic computing with the existing computing infrastructure and systems which run on completely different mechanisms and processing pipelines. Also, the users who have become habituated to the older ways need to be motivated as well as introduced to the new era of computing.
6. Breaking New Ground
The field of neuromorphic computing has seen several notable inventions and advancements over the years. Listed below are a handful of the tech marvels that have paved the way for many more awe-striking inventions to happen.
TrueNorth: TrueNorth is a neuromorphic chip developed by IBM Research. It was designed to copy the function of neurons and synapses of the biological brain. TrueNorth consists of a network of spiking neurons and synapses, enabling efficient and real-time neural network computations. One of its perks is the massively parallel architecture optimized for low power consumption.
Loihi: Loihi is a neuromorphic research chip developed by Intel Labs. Its note-worthy feature is its specialized architecture which is designed to support the efficient simulation of spiking neural networks.
SpiNNaker: SpiNNaker (Spiking Neural Network Architecture) is a parallel computing architecture developed by the University of Manchester. It comprises thousands of small processors that work together to simulate spiking neural networks and are being used profusely in the fields of robotics and neuroscience research.
Akida: Akida is a neuromorphic processor developed by BrainChip. It also combines digital and analogue circuits to mimic the behaviour of natural neuron units, enabling low-power and real-time processing for applications such as image and audio recognition.
Spikey: Spikey is a neuromorphic chip developed by researchers at Heidelberg University. This new-age technology has been used for various applications including investigating information processing in the brain and exploring neuromorphic algorithms.
What We Conclude…
Having a future without computers is nearly impossible. But being able to control them with our minds is the possibility that we are concerned about. We are looking forward to a tech world that gives humans the power to command and superintend computers with just one thought.