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Semiconductors, like silicon, are vital for electronics and AI, enabling fast data processing, memory retention, energy efficiency, and complex computations in smartphones
Semiconductors provide the computational power necessary for AI algorithms to perform complex tasks quickly and efficiently. (Representative/News18 Hindi)
How does our smartphone respond immediately after hearing our voice, or how does Netflix know which movie we would like to watch? The answer is ‘semiconductors’. Let us explore what a semiconductor is and why there is so much fuss about it around the world.
Semiconductor: Neither a Complete Conductor Nor a Complete Insulator
To understand a semiconductor, consider the analogy of a street food vendor. Imagine the vendor is not always inclined to serve everyone – sometimes they accept payment, sometimes they offer food for free, and sometimes they decline service altogether.
A semiconductor behaves in a similar manner: sometimes it allows electricity to flow, sometimes it obstructs it, and sometimes it regulates its speed. This unique property is the reason why it has become the most crucial component in electronic devices.
Its most popular form is silicon – the material used to make computer chips, without which our smartphones would be nothing more than plastic boxes.
Why are Semiconductors Crucial for AI?
Artificial intelligence (AI) is often compared to a brain, but even the most sophisticated brain needs a powerful engine to function. This is where semiconductors come in. They are the engines that power AI, enabling it to process, store, and analyse vast amounts of data at incredible speeds.
Imagine searching for a specific book in a vast library. Doing it manually could take hours, but a superfast digital system could locate the book in seconds. This is analogous to the role semiconductors play in AI. They provide the computational power necessary for AI algorithms to perform complex tasks quickly and efficiently.
Now the question arises: why does AI need semiconductors so much? Let’s understand with some examples:
1. Superfast processing: AI has to process data extremely quickly. For example, when we say “OK Google” or “Hey Siri”, our device responds instantly due to the speed of semiconductors.
2. Memory and learning power: For AI to learn effectively, it needs to retain and learn from past data. A prime example of this is how Netflix’s recommendation engine operates: it remembers a user’s viewing history to suggest similar films they might enjoy.
3. Energy savings: AI systems must operate continuously, yet without generating excessive heat and with minimal power consumption. For example, our smartphone runs AI-powered features (like face recognition) but does not overheat because semiconductors are very energy efficient.
4. Machine Learning and Deep Learning: Machine learning and deep learning are integral to the continuous learning process of artificial intelligence (AI). This constant evolution necessitates high-performance chips to handle the complex computations involved. A prime example is self-driving cars, which rely on these advanced chips to process real-time road conditions and make informed decisions.
Without semiconductors, AI would have remained purely theoretical. These small chips, much like an engine in a car, are essential. They not only empower AI to think and learn but also drive its increasing intelligence.