Neuromorphic Computing: its role in machine learning and AI

In July 2019, a group of AI researchers announced an autonomous bicycle, which could avoid obstacles, respond to voice commands, and track people. The self-driving bike itself was not very helpful, but the AI ‚Äč‚Äčtechnology behind it was noteworthy. The bike was equipped with a special type of AI computer, the neuromorphic chip.

Neuromorphic computing was first proposed in 1980, it has been in existence for years but the recent developments in machine learning and AI has led to growing interest in neuromorphic computers. Let us walk through a case study to understand the human brain and how this technology is being used to advance AI.

Case study

In 2020, a research project on Neuro-Biomorphic Engineering Lab and ALYN hospital by Israel caught the attention of tech giants Intel and Accenture. The project aimed to develop a wheelchair-mounted robot arm with adaptive control. The tech giants announced their support for this project by funding them and developing AI models and algorithms using their neuromorphic computing hardware. What are you trying to achieve? Wheelchair users will have a greater sense of independence at a lower cost.

 Here is how this technology works

Neuromorphic computing is one of many fields of study that can be classified as "technology for good." For technologists who want to deliver adaptive and energy-efficient ML and AI models, it's a potential prospect. In addition, this technology can aid in the development of smarter products that are easier to interact with. What role will this technology play in changing the face of AI in the future?

Neuromorphic computing its revolution in ML and AI

Artificial neural networks are built using the properties of biological nervous systems as a basis in neuromorphic computing. It seeks to mimic the neuronal structure of the human brain, which can deal with ambiguity, uncertainty, and contradiction. However, this is a time-consuming procedure. Neuromorphic computing is being driven by a variety of fields working together. Materials science, computer science, device physics, electrical engineering, and neuroscience are among them.

Here are some of the benefits of building neuromorphic chipsets over present AI systems:

  • A high volume of data messages: Neuromorphic chipsets are ideal for continuous data streaming because of their low latency. They do not require the transfer of external data for analysis.
  • Adaptive computing and quick learning: The architecture of these devices allows them to adapt to changing conditions. In addition, neuromorphic systems have the potential to learn quickly.

While today's CPUs and GPUs are powering supercomputers to unprecedented heights and enabling enormous progress in machine learning and AI applications, there are still gaps that researchers must address. To name a few, physical dimensions, transfer learning, and excessive energy usage.  Artificial intelligence developed so far is limited, learning only from data that has been presented to it.

Multiple layers of processing are used in today's machine learning and AI algorithms and deep neural networks. These neural networks' performance improves only as they train on more data, which necessitates a lot of computational power. Neuromorphic AI is a type of artificial intelligence that aspires to usher in a new era of AI applications.

Neuromorphic computers are designed to provide the fastest computation speeds while avoiding the need for large devices and dedicated buildings. It's worth noting that contemporary supercomputers require megawatts of electricity, whereas the human brain uses only 20 watts, which academics are obsessed with recreating in computers.

The way forward

A report by New York, US, June 21, 2021, Globe Newswire, states that, by 2026, neuromorphic chips will be established in every smartphone, while the forecast also says that the business of these chips will rise by USD 1,560.3 Million. These numbers showcase that neuromorphic computers are way ahead in artificial intelligence-based development and research.