Neuromorphic chips is an exciting new science that aims to mimic the neural structure and operation of the human brain and applications based on these have the capacity to change our world for good.
This novel computing paradigm has the potential to revolutionize a wide range of industries and applications.
In this post, we will explore some key Applications where neuromorphic chips are creating an impact.
Read more: Explained: What the hell is Neuromorphic Computing
What is a neuromorphic chip?
A neuromorphic chip is a specialized type of hardware designed to mimic the structure and function of the human brain. These chips are inspired by the biological neurons and synapses found in the brain, and they are optimized for processing data in a way that resembles the brain’s neural networks.
Unlike traditional digital processors, which use binary logic, neuromorphic chips work with analog signals and can perform parallel processing, making them well-suited for certain types of tasks. Let’s explore this concept using a real-life example and applications.
Real-Life Example of applications of Neuromorphic Chips:
Imagine you’re trying to teach a robot to navigate a complex and dynamic environment, such as a crowded city street. Traditional computer processors might struggle with the sheer amount of data and calculations needed to interpret sensor inputs, make decisions, and adapt to rapidly changing situations.
In contrast, a neuromorphic chip in the robot’s brain could process the sensory data in a way that resembles how our brains work. It would consist of a network of artificial neurons and synapses that fire in response to different inputs, allowing the robot to recognize objects, anticipate movements, and react in real time. This enables the robot to make quick decisions, adapt to unexpected obstacles, and navigate the environment efficiently.
1. Medicine
Fig 1 – Schematic flow of a drug delivery system
Neuromorphic devices are extremely effective at receiving and responding to data from their environment. When coupled with organic materials, these devices become compatible with the human body. In the future, neuromorphic devices could be used to improve drug delivery systems. Their highly responsive nature would allow them to release a drug upon sensing a change in body conditions (i.e. varying insulin and glucose levels).
Moreover, neuromorphic computing has the potential to revolutionize medical devices. Neural implants using these processors could analyze brain signals in real-time, potentially allowing more natural responses in prosthetics and restoring functionality in paralyzed limbs.
The use of neuromorphic chips in analyzing EEG readings (EEG records brainwaves through electrodes attached to the scalp) could aid in diagnosing neurological disorders by recording brainwaves and offering insights into cognition, emotions, and disorders.
Fig 2- Acquisition of brain signals using neuromorphic receptors
The fast pattern recognition abilities of neuromorphic systems make them well-suited for processing intricate biosignals. As our understanding of the brain advances, there’s potential for neuromorphic computing to mimic neural processes, possibly contributing to tissue regeneration.
Read more: 5 Emerging Trends in VLSI That Will Make You In-Demand
2. Large Scale Operations & Product Customization
Neuromorphic computing also holds promise for improving automation and efficiency in factories and large-scale operations. Because neuromorphic chips can process complex sensor data in parallel, they could enable real-time adaptation in smart manufacturing systems.
Rather than just following pre-programmed routines, neuromorphic controllers could flexibly adjust production processes in response to variabilities or faults on the fly. This could improve quality control and lead to more optimized workflows.
Additionally, the pattern recognition capabilities of neuromorphic systems allow for more customized and targeted product designs geared toward specific consumer needs and tastes.
3. Artificial Intelligence
In many ways, the development of advanced artificial intelligence started the push towards neuromorphic computing. While traditional von Neumann architectures have powered deep learning so far, they are still very limited compared to biological neural networks.
Neuromorphic hardware with its event-based processing and spike signaling more closely resembles how information is processed in the brain. This will enable more lifelike AI that can perceive and interact with the environment in real-time.
The applications in the field of AI are right in front of us in the form of generative AI tools like ChatGPT, Mid-journey AI, and training of Autonomous driving software.
4. Imaging
Fig. 3 – Human eye-inspired image acquisition with neuromorphic chips
Neuromorphic vision sensors emulate the human eye’s image production. They work on an event-based system, responding to external light intensity rather than internal signals to create images. Their speed isn’t tied to a frame rate, and each pixel in a neuromorphic sensor acts independently from those around it. Changes in pixels are communicated almost instantly within the device.
This approach allows highly efficient data use and eliminates issues like motion blur or delayed environmental responses seen in traditional sensors. These attributes could make neuromorphic vision sensors a valuable addition to virtual and augmented reality technology.
Neuromorphic processors can construct 3D representations using data from multiple imaging sources, for example creating holistic views in microscopy. This is possible due to the capabilities of neuromorphic hardware to fuse cross-modal sensory information.
5. Edge AI applications
Due to its low energy consumption, neuromorphic computing could be suitable for use on edge AI applications. Here edge computing means computing at the same place where data is generated.
IoT devices generate huge real-time data streams through embedded sensors. Managing this data is challenging. Neuromorphic chips are an ideal computing solution. Their architecture processes massive sensor data in parallel, in real-time, and locally without needing the cloud.
The low power consumption of these chips is an added advantage for battery-powered IoT devices. So, by enabling efficient on-device intelligence, neuromorphic chips unlock more value from IoT networks.
Driverless cars also may not be connected to the internet always as they may be in the outskirts of a town while in commute. So, when not connected to a stable internet source, neuromorphic computing could help them respond more effectively to their surroundings. A system employing neuromorphic computing could take over these vehicles and make driverless cars safer and more suitable for varying environments.
Fig. 4 – Tesla’s Dojo supercomputer to train their autonomous driving software
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Conclusion
Thus medicine to manufacturing, artificial intelligence to imaging, and neuromorphic chips and applications opens up exciting possibilities across multiple fields.
As research in this area continues, we are sure to see many more revolutionary applications of this brain-inspired computing paradigm.
The future looks bright for neuromorphic systems that promise to meet the growing computational demands of our technology-driven world.
