By – Dr. Saswat Kumar Ram
Assistant Professor, Department of Electronics and Communication Engineering, SRM University – AP (Amaravati)
Artificial Intelligence is rapidly becoming part of everyday life. From smartphones and healthcare to automobiles, education and financial services, AI is changing the way we live and work. However, behind every AI application is a powerful semiconductor chip that performs millions or even billions of calculations. As AI becomes more capable, one important question is emerging: How much energy will all this intelligence require?The future of computing may therefore not simply depend on making chips faster. It will depend on making them smarter and more energy-efficient.
The hidden energy cost of AI
Modern AI systems require enormous computing resources. Training and operating advanced AI models can involve large data centres containing thousands of processors. These processors consume electricity and generate significant amounts of heat.This creates a major challenge for the semiconductor industry. Increasing computing performance traditionally meant adding more transistors, increasing operating frequencies and processing more data. But these approaches also increase power consumption.For devices such as smartphones, wearable electronics, sensors and Internet-of-Things devices, the challenge is even greater. Many of these devices operate on batteries or limited energy sources. A chip that consumes less power can directly translate into longer battery life, lower operating costs and more sustainable technology.
Can AI help design better chips?
Interestingly, the technology creating this growing demand for computing can also become part of the solution.Designing a modern semiconductor chip is an extremely complicated process. Engineers have to make thousands of decisions involving circuit architecture, transistor sizing, placement, routing, timing, power consumption and thermal behaviour.Artificial Intelligence and Machine Learning can assist engineers in exploring these enormous design possibilities.Instead of relying entirely on manual trial and error, AI algorithms can analyse previous designs, identify patterns and predict which design choices are likely to produce better results. Engineers can then use these predictions to search for solutions that consume less energy while maintaining the required performance.This approach is increasingly being described as AI-assisted chip design.
From faster chips to smarter chips
For many years, semiconductor development was strongly driven by the goal of achieving higher processing speed. Today, the focus is gradually expanding.A successful chip must balance three important factors: performance, power and area. Engineers commonly refer to this as PPA—Power, Performance and Area.A chip that is extremely fast but consumes excessive energy may not be practical. Similarly, a very low-power chip may not be useful if its performance is insufficient.AI-based optimization can help engineers find a better balance between these competing requirements.For example, an AI system could evaluate different circuit configurations and recommend one that delivers nearly the same computing performance while consuming significantly less energy.
Why low-power chips matter for India?
For India, energy-efficient semiconductor technology has importance beyond the technology sector.India is rapidly expanding its semiconductor ecosystem, with investments in chip manufacturing, design, electronics and related industries. At the same time, the country is experiencing growing demand for digital services, data centres, electric vehicles, smart infrastructure and connected devices.Energy-efficient semiconductor design can therefore contribute to both economic growth and energy sustainability.The development of indigenous expertise in low-power chip design can also create opportunities for universities, startups and semiconductor companies to develop products specifically suited to India’s requirements.
The future: intelligence at the edge
One of the most exciting developments is the movement of AI from large data centres toward smaller devices.Instead of sending every piece of information to the cloud, future devices may perform more AI processing locally. This concept, often called edge AI, can make systems faster, more private and less dependent on continuous internet connectivity.But edge devices have strict energy limitations.A smart camera, wearable device, agricultural sensor or autonomous system may need to perform sophisticated AI calculations while operating on a small battery. This makes low-power semiconductor design essential.The challenge is therefore not simply to create more powerful AI. It is to create AI that can operate efficiently almost anywhere.
A new direction for semiconductor innovation
AI-driven low-power VLSI design represents an important shift in the way semiconductor technology is developed. Rather than relying exclusively on conventional design techniques, engineers can increasingly use AI as a partner in the design process.The objective is simple but ambitious: more intelligence, better performance and lower energy consumption.As AI becomes an integral part of our lives, the chips supporting it must evolve as well. The next generation of semiconductor innovation may therefore not be defined only by how many transistors a chip contains or how fast it can process information.It may ultimately be judged by a more important question:How much intelligence can we deliver for every unit of energy we consume?That question could shape the future of AI, semiconductors and sustainable computing.
