Over the next decade, neuromorphic computing will evolve to become the cornerstone of artificial intelligence, driving machine decisions without any ambiguities.
The year is 2030, and in this new world of accountability where communication, time and knowledge are the chief strengths of humanity, society is thriving. Long gone are the days of dragging a smartphone and booking a cab. Long gone, also, are the days of rushing to the kitchen and switching off the pasta. We realize that neuromorphic computing, IoT, was the magic key to help us translate our thoughts to machine vision in real-time.
If we are assuming that we are living in 2030, today we are immediately able to connect with digital interfaces. At an instant, we are able to impart our schedules to the smart assistant such as Google Home or Alexa. Neuromorphic computing, a juggernaut of neuroscience, microelectronics and machine learning, mimicking the human brain in the form of an artificial neural system, has opened a myriad of possibilities. Here are a few.
With the help of neuromorphic computing, doctors are able to focus on the right regions of diagnosis and therapeutic reports with recommendations from the AI assistant.
To understand how Neuromorphic Computing works, we need to understand how the human brain functions. The human brain consists of billions of cells within the nervous system called neurons. These neurons communicate with each other through a unique encoded signal. You can watch the below video to understand how the neurons in the brain work.
Just as the neurons communicate with other neurons through synapses, a neuromorphic chip also analyzes signals using artificial nodes that replicate synapses. Ultimately, neuromorphic computing is all about an organic electrochemical transistor that manipulates the output basis changes in the input.
Today, in 2030, neuromorphic chips have swiftly moved from laboratories to commercial applications. The real contribution of Neuromorphic Engineering is to boost the accuracy of Artificial Intelligence decisions that resemble random human behavior. It is purposed to correct any conceivable bugs in deep learning with proper diligence.
Loihi, Intel produced neuromorphic architecture, can emulate the neural structure of the human brain, uncovering and adapting accurately to uncertainties of human decisions.
Gone are the days when the autonomous driving car used to slow down to ask a clarifying question on the speed every time it comes across a kid on the roadside. In 2030, cars are super intelligent; they accurately predict human intention and dynamic actions which are probabilistic in nature. Unlike the deterministic models of deep learning in the past, neuromorphic computing has enabled intuition and prediction in AI applications, establishing trust in the confidence of AI decisions.
Assumed 2030, the $6.48 billion market valuation of neuromorphic computing done by Grand View Research was realized by 2025, doubling applications with global adoption in almost every field. Shining a light on the application of neuromorphic computing in the automotive industry, autonomous vehicles, today in the year 2030, are learning faster than ever by analyzing spiking neurons in a human brain and recording together purpose or intent which only occur in the case of unusual behavior.
As Moore’s Law pushed chip designers to pack more transistors onto circuits, the number of interconnections between those transistors multiplied over and over again. Even the no. of cores increased in day to day computing, the chips are not used as efficient it can be. One clock cycle cannot communicate between all the logic transistors in these chips. Alternatively, Neuromorphic computing is achieved by creating a “Neuristor” circuit which behaves in the same way how the neurons in the brain work.
Unlike how Apple’s Siri virtual assistant uploads speech to the cloud for processing, AI-powered smart assistants in 2030 are able to instantly simulate the way biological neurons function. With the help of neuromorphic computing, machine learning systems have become portable that does not require a rack full of servers for processing.
Considered 2030, AI-powered smartphones carry their machine learning systems at the edge that enable the cognitive power of neural networks to respond within split-seconds. Not surprisingly, our smartphone assistants can accurately and promptly decide on actions such as operating the thermostat, lights, or security system.
Considering the characteristics of fast computing speed with low power consumption methods, neuromorphic computing has huge potential in increasing the accuracy of cognitive applications. Even though neuromorphic computing is at a theoretical level, ongoing research has already brought about glimpses of promising marketable applications and commercial products. It is a potential direction that would revolutionize computing power over the next decade.
About the author(s)
Karthikeyan Prakash is a Product Manager in Techolution’s India office. He contributes to the firm’s AI and IoT Research and Product Development practice and helps clients across industries significantly improve their returns from IoT investments. Karthikeyan has an MBA in Finance from the Birla Institute of Technology and Science, Pilani and a BE in Electronics and Communications Engineering.