{"id":5407,"date":"2025-09-28T19:25:37","date_gmt":"2025-09-28T19:25:37","guid":{"rendered":"https:\/\/axivtech.com\/?p=5407"},"modified":"2025-10-08T13:19:38","modified_gmt":"2025-10-08T13:19:38","slug":"non-ai-chips-could-power-the-next-generation-of-devices","status":"publish","type":"post","link":"https:\/\/areeblog.com\/non-ai-chips-could-power-the-next-generation-of-devices\/","title":{"rendered":"Non-AI Chips Could Power the Next Generation of Devices"},"content":{"rendered":"<p><img loading=\"lazy\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"5408\" data-permalink=\"https:\/\/areeblog.com\/non-ai-chips-could-power-the-next-generation-of-devices\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683\/\" data-orig-file=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1.jpg\" data-orig-size=\"1024,683\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024&amp;#215;683\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1.jpg\" class=\"aligncenter size-full wp-image-5408\" src=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1.jpg\" alt=\"Why Non-AI Chips Could Power the Next Generation of Devices\" width=\"1024\" height=\"683\" srcset=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1.jpg 1024w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1-300x200.jpg 300w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1-768x512.jpg 768w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1-330x220.jpg 330w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1-420x280.jpg 420w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1-615x410.jpg 615w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/smart-microchip-background-motherboard-closeup-technology-remix-1-scaled-1-1024x683-1-860x574.jpg 860w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p>Global semiconductor sales are on a sharp upward path: industry forecasts put 2025 revenues near <a href=\"https:\/\/www.deloitte.com\/us\/en\/insights\/industry\/technology\/technology-media-telecom-outlooks\/semiconductor-industry-outlook.html\">$697 billion<\/a>, following a strong 2024.<\/p>\n<p>That number includes everything from datacenter GPUs to tiny microcontrollers. But not all growth looks the same.<\/p>\n<p>A lot of real <a href=\"https:\/\/areeblog.com\/google-pixel-10-pro-fold\/\">product innovation<\/a> 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.<\/p>\n<figure id=\"attachment_5412\" aria-describedby=\"caption-attachment-5412\" style=\"width: 960px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"5412\" data-permalink=\"https:\/\/areeblog.com\/non-ai-chips-could-power-the-next-generation-of-devices\/us188006_figure1\/\" data-orig-file=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1.jpeg\" data-orig-size=\"960,755\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"US188006_Figure1\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1.jpeg\" class=\"wp-image-5412 size-full\" src=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1.jpeg\" alt=\"future of non-AI chips. Chips market prediction\" width=\"960\" height=\"755\" srcset=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1.jpeg 960w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1-300x236.jpeg 300w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1-768x604.jpeg 768w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/US188006_Figure1-860x676.jpeg 860w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/><figcaption id=\"caption-attachment-5412\" class=\"wp-caption-text\">Credit: Deloitte<\/figcaption><\/figure>\n<p>These are the microcontrollers, digital signal processors, compact NPUs and mixed-signal ASICs that I\u2019ll 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.<\/p>\n<p><strong>Key takeaways:<\/strong><\/p>\n<ul>\n<li>Device classes such as phones, wearables and vehicles favor low-power, task-specific silicon for battery life, latency and cost.<\/li>\n<li>Training large models remains a datacenter activity; inference at the edge often runs on lightweight NPUs, DSPs and MCUs integrated into SoCs.<\/li>\n<li>Chiplet architectures and advanced packaging let designers mix nodes and IP to hit efficiency and price targets.<\/li>\n<li>Foundry economics and supply allocation push volume products toward mature process nodes that are cheaper and proven.<\/li>\n<li>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.<\/li>\n<\/ul>\n<h2>Edge Devices Rely on Non-AI Chips to Stay Efficient<\/h2>\n<p>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.<\/p>\n<p>Engineers compare options using energy per useful operation, not raw TOPS or FLOPS.<\/p>\n<p>For tasks such as <a href=\"https:\/\/areeblog.com\/tinyml-and-edge-ai-on-resource-constrained-devices\/\">audio processing<\/a>, 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.<\/p>\n<p>Laboratory studies and system analyses focused on edge deployment show clear energy and latency advantages for targeted silicon in many real-world use cases.<\/p>\n<h2>Training and Inference Follow Different Dardware Paths<\/h2>\n<p>Training large neural networks concentrates computation in datacenters that use high-throughput accelerators.<\/p>\n<p>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.<\/p>\n<p>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 <a href=\"https:\/\/areeblog.com\/microsoft-integrates-gpt-5-into-copilot\/\">smart features<\/a> without needing datacenter power.<\/p>\n<p>Qualcomm, for example, publishes materials showing <a href=\"https:\/\/www.qualcomm.com\/content\/dam\/qcomm-martech\/dm-assets\/documents\/Unlocking-on-device-generative-AI-with-an-NPU-and-heterogeneous-computing.pdf\">on-device NPUs<\/a> and heterogeneous computing chains that balance CPU, GPU and NPU workloads for efficiency.<\/p>\n<h2>Heterogeneous Systems, Chiplets and Advanced Packaging<\/h2>\n<p>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.<\/p>\n<p>This approach reduces manufacturing risk and unit cost, improves yield and shortens development cycles for specialized IP.<\/p>\n<p>Industry analyses and <a href=\"https:\/\/www.grandviewresearch.com\/industry-analysis\/chiplet-market-report\">market reports<\/a> 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.<\/p>\n<figure id=\"attachment_5413\" aria-describedby=\"caption-attachment-5413\" style=\"width: 1025px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"5413\" data-permalink=\"https:\/\/areeblog.com\/non-ai-chips-could-power-the-next-generation-of-devices\/chiplet-market-size\/\" data-orig-file=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size.webp\" data-orig-size=\"1025,576\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"chiplet-market-size\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Chiplet Market Size. Credit: Grand View Research&lt;\/p&gt;\n\" data-large-file=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size.webp\" class=\"size-full wp-image-5413\" src=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size.webp\" alt=\"Chiplet Market size\" width=\"1025\" height=\"576\" srcset=\"https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size.webp 1025w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size-300x169.webp 300w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size-768x432.webp 768w, https:\/\/areeblog.com\/wp-content\/uploads\/2025\/09\/chiplet-market-size-860x483.webp 860w\" sizes=\"auto, (max-width: 1025px) 100vw, 1025px\" \/><figcaption id=\"caption-attachment-5413\" class=\"wp-caption-text\">Chiplet Market Size. Credit: Grand View Research<\/figcaption><\/figure>\n<p>The result for devices is a path to high efficiency without an all-in bet on the most expensive fabs.<\/p>\n<h2>Manufacturing Realities: Node Choice and Volume Economics<\/h2>\n<p>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.<\/p>\n<p>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.<\/p>\n<h2>Emerging Substrates and Data-Movement Alternatives<\/h2>\n<p>Beyond traditional CMOS scaling, several technologies are gaining practical traction for lowering the energy of data movement and certain computations inside packages and racks.<\/p>\n<p>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.<\/p>\n<p>Likewise, <a href=\"https:\/\/www.nature.com\/articles\/s41467-024-51221-z\">analog compute<\/a> and memory-centric designs can perform portions of neural operations with fewer digital toggles, reducing energy for specific kernels.<\/p>\n<p>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.<\/p>\n<h2>Examples From Industry<\/h2>\n<p><a href=\"https:\/\/www.reuters.com\/business\/autos-transportation\/mercedes-benz-spins-out-silicon-valley-chip-group-into-new-company-2025-09-26\/\">Automotive spinout<\/a> focused on low-power chiplets. A recent company launch spun out of an automaker\u2019s R&amp;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.<\/p>\n<p>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.<\/p>\n<p>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.<\/p>\n<h2>What to Watch Next<\/h2>\n<p>If you build products or follow hardware trends, track three indicators:<\/p>\n<ol>\n<li>SoC roadmaps and NPU performance per watt from major vendors, these indicate how much capability will move on-device. Watch vendor whitepapers and datasheets.<\/li>\n<li>Adoption of chiplet standards and packaging volumes, market reports and foundry announcements will show whether modular designs scale across vendors.<\/li>\n<li>Commercial deployments of optical\/analog links inside devices or packages, conference papers and supplier product releases will signal practical shifts in data-movement strategy.<\/li>\n<\/ol>\n<p>These are measurable signals tied to engineering choices and supply chains.<\/p>\n<h2>Closing<\/h2>\n<p>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.<\/p>\n<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":5408,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_monsterinsights_skip_tracking":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[164],"tags":[1045],"class_list":["post-5407","post","type-post","status-publish","format-standard","has-post-thumbnail","category-tech-updates","tag-chips"],"share_on_mastodon":{"url":"https:\/\/mastodon.social\/@Areeblog\/115283584778618248","error":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.4 (Yoast SEO v28.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Non-AI Chips Could Power the Next Generation of Devices - 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