Global semiconductor sales are on a sharp upward path: industry forecasts put 2025 revenues near $697 billion, following a strong 2024.
That number includes everything from datacenter GPUs to tiny microcontrollers. But not all growth looks the same.
A lot of real product innovation such as phones that run longer, cars that make safer, faster decisions, and sensors that stay always on, depends on small, efficient pieces of silicon designed for a single job.
These are the microcontrollers, digital signal processors, compact NPUs and mixed-signal ASICs that I’ll call non-AI chips. They are not about training giant models in the cloud; they are about making devices smarter within strict power, thermal and cost limits.
Key takeaways:
- Device classes such as phones, wearables and vehicles favor low-power, task-specific silicon for battery life, latency and cost.
- Training large models remains a datacenter activity; inference at the edge often runs on lightweight NPUs, DSPs and MCUs integrated into SoCs.
- Chiplet architectures and advanced packaging let designers mix nodes and IP to hit efficiency and price targets.
- Foundry economics and supply allocation push volume products toward mature process nodes that are cheaper and proven.
- Optical interconnects and analog compute are emerging options that reduce energy for certain on-device tasks; they complement rather than replace digital non-AI silicon.
Edge Devices Rely on Non-AI Chips to Stay Efficient
Phones, fitness trackers, industrial sensors and many automotive controllers operate under tight battery and heat limits. That constraint is the dominant engineering reality for device design: a solution that gives maximum throughput is irrelevant if the battery dies in an hour or the chip overheats.
Engineers compare options using energy per useful operation, not raw TOPS or FLOPS.
For tasks such as audio processing, sensor fusion or camera pre-processing, specialized blocks (ISPs, DSPs, tiny NPUs) perform the needed work at far lower joules per result than general-purpose accelerators.
Laboratory studies and system analyses focused on edge deployment show clear energy and latency advantages for targeted silicon in many real-world use cases.
Training and Inference Follow Different Dardware Paths
Training large neural networks concentrates computation in datacenters that use high-throughput accelerators.
In contrast, inference (the act of running a trained model on new inputs) often takes place on the device itself or on nearby edge servers. Device teams therefore prioritize power efficiency, predictability and low latency.
Major SoC vendors have made this split explicit in their roadmaps: they combine a modest but efficient NPU for local inference with dedicated blocks (ISP, audio codec, always-on microcontroller) so the phone or device delivers smart features without needing datacenter power.
Qualcomm, for example, publishes materials showing on-device NPUs and heterogeneous computing chains that balance CPU, GPU and NPU workloads for efficiency.
Heterogeneous Systems, Chiplets and Advanced Packaging
Rather than squeezing everything into one monolithic die, designers increasingly assemble systems from multiple smaller dies (chiplets) inside advanced packages. This modular approach lets teams place the most performance-sensitive logic on leading-edge process nodes while moving I/O, memory controllers and other less demanding blocks to mature, lower-cost nodes.
This approach reduces manufacturing risk and unit cost, improves yield and shortens development cycles for specialized IP.
Industry analyses and market reports show rapid growth in chiplet adoption and supporting packaging technologies, estimates put the 2024 chiplet market in the billions with multi-year high growth projected.
The result for devices is a path to high efficiency without an all-in bet on the most expensive fabs.
Manufacturing Realities: Node Choice and Volume Economics
Leading-edge process nodes require expensive tooling, long lead times and significant wafer cost. Datacenter accelerators and some high-end mobile parts justify the cost, but mass-market devices rarely need bleeding-edge density everywhere on the SoC.
Analyst and industry reports document large volumes of chips produced on mature nodes because they deliver acceptable performance at much lower cost. That creates a stable, high-volume market for non-AI chips targeted at sensing, control and efficient inference, components that must be affordable and reliable at scale.
Emerging Substrates and Data-Movement Alternatives
Beyond traditional CMOS scaling, several technologies are gaining practical traction for lowering the energy of data movement and certain computations inside packages and racks.
Optical interconnects move large amounts of data with different energy characteristics than electrical wiring, which can reduce the cost of connecting dies in a package or across short datacenter distances.
Likewise, analog compute and memory-centric designs can perform portions of neural operations with fewer digital toggles, reducing energy for specific kernels.
These technologies are early in broad product adoption, but they are being actively researched, prototyped and commercialized, making them plausible complements to non-AI silicon in future device stacks.
Examples From Industry
Automotive spinout focused on low-power chiplets. A recent company launch spun out of an automaker’s R&D team, explicitly aiming at low-power, high-reliability chips for vehicles and drones, and citing chiplets and energy reductions as central goals. That move underlines the commercial interest in domain-specific, efficient silicon for mobility.
SoC vendors balancing NPUs and dedicated blocks. Major smartphone SoC roadmaps emphasize a heterogeneous mix (NPUs for on-device models, ISPs for camera tasks, and DSPs for audio) rather than a single large accelerator. Public product documents and whitepapers describe that integration strategy.
Market growth in chiplet and packaging services. Multiple analyst reports document rapidly growing market projections for chiplet technologies and advanced packaging, reflecting industry demand for modular, cost-efficient system assembly.
What to Watch Next
If you build products or follow hardware trends, track three indicators:
- SoC roadmaps and NPU performance per watt from major vendors, these indicate how much capability will move on-device. Watch vendor whitepapers and datasheets.
- Adoption of chiplet standards and packaging volumes, market reports and foundry announcements will show whether modular designs scale across vendors.
- Commercial deployments of optical/analog links inside devices or packages, conference papers and supplier product releases will signal practical shifts in data-movement strategy.
These are measurable signals tied to engineering choices and supply chains.
Closing
The broad reality is straightforward and verifiable: different parts of the compute stack solve different engineering problems. Datacenter accelerators win on raw throughput and are essential for training large models.
For many next-generation devices, however, the constraints of battery, heat, latency and unit cost make specialized, efficient silicon the sensible route. That is why devices will increasingly rely on a mix of non-AI chips and modest NPUs, assembled into heterogeneous systems that prioritize practical efficiency over raw peak numbers.
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