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They say perpetual motion is impossible because it is impossible for a machine to do work without an external energy source. The moment the energy is cut, the machine stops as well. That was just an analogy. Let us look at something else now, the symbiotic relationship between AI and semiconductor chips.
The AI-Microchip Symbiosis
Is it possible to achieve continuity in an unperturbed system, where improved semiconductor design powers AI and AI helps enhance semiconductor design, each powering the other in a continuous cycle to perfection? What will be the result of such a cycle, and how will it impact mankind?
Such a continuous cycle might already be a possibility? We may already be in a cycle that might never stop as the pursuit of perfection continues unhindered. We will now look at one aspect of this continuous cycle in the article, how semiconductors are powering next-gen AI and what the impact is likely to be.
5 Ways Semiconductors Will Power Next-Gen AI
The microchip, as it continued its evolution, is a wonder, accommodating more and more transistors and other components in a tiny space, becoming tinier and powerful over time. But then AI entered the scene, and it now no longer seems possible to address the computational needs of AI just by squeezing more and more into a tinier space.
1. Optimized Chip Design
The global chip design market is estimated to reach $602 bn in valuation by 2033. But we have our limitations, and AI needs parallel processing power, and a lot of energy is used in the process. So, the focus has shifted a bit from just brute-forcing the approach to thinking about it more deeply. The architecture has changed. Semiconductor technology is also changing to accommodate new chip design paradigms. This is encouraging. Where GPUs were used for AI training, we may see more and more ASICs (like Google’s Tensor) and FPGAs being used in AI.
2. Neuromorphic Computing
The brain processes information in a different way than a microchip does. Neuromorphic computing mimics the brain’s way of functioning. Neuromorphic systems typically integrate the memory and computing parts, leading to processing-in-memory, reducing the data movement, and encouraging parallel processing. It also makes the chip more energy efficient, computing only when there is a need to.
3. 3D Stacking
This is more about packing than actual design, but it influences design and functionality as well. By stacking multiple microchips vertically, you can form a single IC package, and how does this exactly benefit AI? First, it reduces the footprint, enabling more data processing in a smaller space. With AI data centers expanding, this could be a great space saver! There is also a higher degree of design freedom, and the development time is shortened when it comes to 3D stacking. By optimizing the design of chips, we can further accelerate the growth of AI, as discussed earlier.
4. Edge Computing
We discussed how semiconductors will help train AI in dedicated centers. Now, let us talk about local devices. Edge AI, which is a result of advanced semiconductor tech, enables the necessary processing power and energy efficiency in a smaller form factor. In edge computing, the processing happens at the edge, locally rather than transmitting the data to a central location. Hardware accelerators enable this to work very easily. The newer semiconductor chips have made this possible, as they are made to work on battery-efficient devices just as well as other edge devices that consume power.
5. Generative AI in Chip Design
This brings us to the point that we started our discussion with. With Generative AI, we are already helping several sectors, especially the food and beverages industry, in making customer-centric packages and improving the taste or even the life of the product. Generative AI can similarly optimize the chip design, helping in the power and thermal management and improving the performance of the chip as well. It also streamlines verification, analyzing the chips in final quality checks.
Let us now analyze the impact of semiconductor innovations on next-gen AI in three key areas.
Impact of Semiconductor Innovations on AI-Driven Sectors
1. Revolution in automotive electronics
There is a shift from a mechanical system on wheels to an electronic system on wheels. The ICs support high-performance computing, and the smart peripherals support OTA,s which enable the system to function at its best. Understanding the environment is another enablement due to advancements in semiconductor technology. The sensors pick up any signs of rain or poor visibility and adjust the driving accordingly in an autonomous mode through AI in real-time.
2. Rise of Smart Grids
The rise of renewable energy generation to satisfy the demands of electric vehicles in a country with a rapidly growing population like India has also led to the development of smart grids. And why exactly? Because renewable energy is not always available with the same intensity throughout the day, there needs to be smart grids powered by AI, which will help meet the changing demands and balance the workloads accordingly.
3. Emergence of Efficient Motor Control
Motor control happens through remote-controlled actuators and sensors that respond to conditions in the immediate environment, such as heat generated to control the motor in the right manner. It can also shut off automatically when the purpose is served, as in when a certain water level is reached, if it is a water pump that the motor is driving. Here, too, AI finds an application, especially with fluctuations in water demand, where it can reduce water supply and be more energy efficient.
Future of the Chip and AI
What does the future of advanced microchip-powered AI look like? We may need to take a picture out of a science fiction novel, and we may still underestimate what the future will look like. The future will also be decided by policy, the shift in governments, the shift in favor of technologies, the right stance on sustainability, etc. But as it stand,s this symbiotic relationship between the microchip and AI will continue to strengthen our future for decades to come. How far it will continue, only time will tell.
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Rajkumar Shanmugam
Rajkumar Shanmugam is the Head of HR at ALP Consulting, bringing over 19 years of comprehensive HR leadership experience across India and international markets. His expertise spans talent acquisition, employee relations, performance management, compliance, and HR transformation. Rajkumar has a proven track record of driving people-centric initiatives, enhancing workplace culture, and aligning HR strategy with business goals. With extensive experience in US staffing operations and global mobility, he continues to lead organizational excellence through innovation and employee engagement.
