Timely technology explainer | Updated 20 September 2026
Semicon India 2026 concluded in New Delhi on 19 September, bringing India’s semiconductor ecosystem back into the technology headlines. For an ordinary laptop or smartphone buyer, however, terms such as “AI chip,” “neural engine” and “NPU” can still be difficult to compare.
This article explains those labels without recommending a particular company or semiconductor stock. Event figures below are attributed to current reporting because a matching primary event report was not accessible during this check.
What happened at Semicon India
Financial Express reported on 20 September 2026 that the fifth Semicon India was held from 17–19 September at Yashobhoomi, Dwarka. The report attributes the event organisation to the India Semiconductor Mission, MeitY and SEMI.
The reported registration and participation figures should be read as event-organiser statements relayed by the publication, not as independently audited counts. The relevance for consumers is broader: smartphones, vehicles, network equipment and AI systems all depend on semiconductor design and manufacturing.
Start with the CPU
The central processing unit, or CPU, is a general-purpose processor. It manages operating-system work and a wide range of application instructions. CPUs can run AI workloads, but a specialised processor may execute certain repeated mathematical operations more efficiently.
A faster CPU does not automatically mean a device has the best AI performance, and the presence of an NPU does not make the CPU unimportant. Actual applications often divide work among several processors.
Where the GPU fits
A graphics processing unit, or GPU, is built for large amounts of parallel computation. That characteristic made GPUs useful not only for graphics but also for training and running many machine-learning models.
Desktop and data-centre GPUs can handle demanding workloads, while integrated mobile GPUs operate within tighter power and thermal limits. Comparing only the processor category therefore reveals little about sustained performance.
What is an NPU?
A neural processing unit is a specialised accelerator designed for selected neural-network operations. In compatible software, an NPU can process tasks such as background effects, speech features, image enhancement or model inference while using less power than a less-specialised route might require.
The phrase “AI PC” or “AI phone” should not be treated as a universal performance grade. Different manufacturers measure accelerator throughput differently, and software support determines whether an application actually uses the available NPU.
Why TOPS alone is incomplete
TOPS means trillions of operations per second. It can describe theoretical accelerator throughput under stated conditions, but the number alone does not show model compatibility, precision, available memory, sustained power use or application performance.
Before comparing two TOPS claims, check what hardware is being counted and whether the measurements use comparable conditions. A combined “platform AI” figure may not equal the dedicated NPU figure.
Questions Indian buyers should ask
- Which applications currently use the NPU on this exact device?
- Does the desired feature run locally, in the cloud or through both?
- Which Indian languages are officially supported?
- Does the feature require a separate subscription?
- What happens when internet connectivity is unavailable?
- How long does the manufacturer promise software and security updates?
Check the device maker’s current specification and application documentation. A demonstration performed on a different configuration may not represent the model sold at the price you are considering.
AI hardware does not guarantee AI accuracy
An accelerator can run a model efficiently, but it does not guarantee that the model’s answer is correct. Output quality also depends on the model, input, language, software implementation and evaluation process.
For a practical comparison, use the same non-sensitive task on shortlisted devices and check output, latency and battery impact. Such a personal comparison helps with your use case but should not be presented as a controlled laboratory benchmark.
Conclusion
India’s semiconductor push makes AI-chip terminology increasingly relevant, but buyers still need feature-level evidence. Understand the roles of CPU, GPU and NPU, then check compatible software and real requirements. An “AI” badge is the beginning of the comparison, not the conclusion.
Sources and verification
- Financial Express: Semicon India 2026 report, published 20 September 2026
- IBM: Semiconductor explainer
Fact-check: 20 September 2026, evening IST. Event dates and publication date were checked separately. Reported attendance figures remain attributed to the event report.
