The human brain has long been an object of fascination and curiosity for researchers. The way it functions and efficiently supports the cognitive human abilities with biological energy as well as neurons as the basic unit has always been a stimulation.
Inspired by the brains fast computational speed, scientists have designed neuromorphic computing chips which specifically mimics the human brain and could be the stepping-stone in the evolution of artificial intelligence and computing.
After decades of research and collaboration, Intel introduces the first-of-its-kind self-learning neuromorphic computing chip which can also be called ‘a brain of silicon’. It is a technology which will help people understand human brains more efficiently while maintaining the energy efficiency and cost performance benefit you get from human brain.
Neuromorphic chips are basically microprocessors whose architecture is similar to that of a human brain model where the network of neurons are interconnected and the connections between them are called synapses. It is one of the fastest growing technology in the market with an approximate reach to $1.78 billion by 2025.
The concept behind the invention was to develop computing circuits that resemble human brains which can unlock new possibilities by making the world connected and smarter with experience. According to Intel, the chip can learn and adapt to things on the go. Unlike other machine learning systems that require deep-learning and intensive training of data using huge clusters of computers, neuromorphic computing chip will be a self-learning chip.
Imagine a future where complex decisions could be made faster, where robots are more autonomous, where stoplights can automatically sync their timings to the flow of the traffic, where cameras can look for a missing person. The Intel researchers think that the neuromorphic computing chip can help the world get smarter over time by using the real-time data to learn and thus redefine the classic compute platforms.
The high energy efficient chip elevates the bar for artificial intelligence by taking an innovative approach to computing via asynchronous spiking. The spikes make the chip event-driven which operates only when needed to thus resulting in a better operating environment and low energy consumption. These chips not only consume low power but are also good at tasks that need pattern-matching over super-computing for example self-driving. In the future, these chips can prove an efficient solution for processing and analysing the huge amount of data generated by sensor networks and self-driving cars.
So, where exactly neuromorphic chips are applicable? The technology is ideal for analysis based tasks such as cognitive computing, adaptive AI, data-sensing and associated memory. Neuromorphic computing chip is the future of AI reducing its diverse and complex workloads.
Though how exciting and potential all these sounds, neuromorphic chips will still take time to come out in the commercial markets, but when it will, it will definitely redefine the AI and computing platforms!