Arm SME2 & KleidiAI: Real‑Time On‑Device AI Acceleration for Every Smartphone

Breaking News: Arm Unveils On‑Device AI Engine That Turns the CPU Into a Real‑Time Intelligence Core

Arm announces a decisive shift in mobile AI. The Scalable Matrix Extension 2 (SME2) now enables real‑time on‑device AI directly on the CPU, changing the way apps think and respond on billions of smartphones.

Developers will notice AI tasks no longer rely on cloud processing or high power for latency. SME2 expands deep learning workloads to run locally, delivering faster results while preserving privacy and battery life.

Acceleration That Works Anywhere, Automatically

SME2 is activated through Arm KleidiAI, which integrates with widely used frameworks. In practice, developers can run AI workloads on the Arm CPU without rewriting strategies for different devices.

By extending Armv9 with dedicated matrix‑processing instructions, SME2 enables devices to execute dense math operations common in language and vision models. The result is speed, efficiency, and a more seamless user experience.

What It Means for users and Apps

The gains are tangible. Real‑time AI responses can be up to five times faster, while speech workloads see about 4.7 times lower latency. Audio generation speeds improve by roughly 2.8 times, with noticeable power savings in routine mobile AI tasks.

Iconic apps illustrate the impact. Some services can generate travel previews on‑device, and others can provide real‑time summarization and translation without a network round‑trip.

Build Once, Run Everywhere

Fragmentation is a longtime challenge for mobile AI. SME2 standardizes acceleration on the CPU,offering a portable foundation across iOS and Android devices alike.

From flagship models to mid‑range smartphones powered by Arm CPUs, developers gain predictable performance. Testing becomes simpler, regressions rarer, and time‑to‑market faster.

As Arm updates its architecture, KleidiAI continues to push performance gains automatically, requiring no code rewrites. flip SME2 on and watch apps accelerate without extra work.

The Next Frontier of On‑Device Intelligence

On‑device AI is less about cramming more intelligence into hardware and more about bringing smart capabilities closer to the user. Real‑time, private inference is now feasible on the device itself.

With SME2 and KleidiAI, developers can narrow the gap between user action and bright response. The CPU becomes a ready‑made AI engine that keeps pace with every tap and interaction.

Key Facts at a Glance

Aspect	Details
Core technology	Arm scalable Matrix Extension 2 (SME2)
Execution environment	On‑device CPU using Armv9 with matrix processing
Framework integration	KleidiAI; PyTorch; ExecuTorch; ONNX Runtime; Alibaba’s MNN; Google LiteRT
Platform reach	Allows AI workloads across billions of devices powered by Arm CPUs
Performance gains	Up to 5x faster AI responses; 4.7x lower latency for speech; 2.8x faster audio generation
Power impact	Notable power savings in common mobile AI scenarios

Why This Matters—Evergreen Perspectives

For developers, SME2 offers a stable foundation that scales with device generations, easing maintenance and speeding delivery. For users, it means faster, more private experiences that don’t drain batteries or overheat devices.

Security and privacy take a front seat when processing happens locally. Reduced cloud dependence also means less data exposure and lower network bandwidth needs.

As more apps demand conversational AI, real‑time video interpretation, and on‑device translation, the industry gains a blueprint for keeping intelligence close to the user without compromising performance.

What Developers Should Watch Next

Expect ongoing improvements as Arm evolves its architecture. KleidiAI will automate performance boosts,letting apps reap updated benefits with minimal friction. The race now centers on how quickly teams can adopt SME2 across their AI pipelines and expand on‑device capabilities.

Q1: Which feature would you prioritize for on‑device AI in your next app—faster responses, lower latency, or energy efficiency?

Q2: how would you balance on‑device AI with occasional cloud offloading to maximize privacy and performance?

Breaking news for mobile developers and users alike: the CPU itself is becoming a premier AI engine, delivering faster, smarter experiences were it matters most—on the device.

Share your thoughts and tell us which app use case you’re most excited to bring on‑device with SME2.

Understood

Arm SME2 – A Deep dive into Scalable Matrix Extension 2

What is SME2?

• A hardware‑level matrix compute engine built into Arm v9‑A CPUs.
• Extends the original Scalable Matrix Extension (SME) wiht:

1. Larger vector registers (up to 2048 bits).

2. Dynamic tiling to match AI workload shapes.

3. Zero‑overhead data‑movement instructions that keep tensors in‑register.

Key capabilities

• Real‑time inference for vision‑transformer (ViT) and convolutional networks without off‑chip memory stalls.
• Mixed‑precision support (FP16/FP8/BF16) that auto‑scales based on power budget.
• Native support for sparsity – hardware‑accelerated mask handling reduces MAC count by up to 70 % for pruned models.

Why SME2 matters for smartphones

• Enables on‑device AI that rivals low‑power GPUs while consuming < 1 W under typical camera‑AI loads.
• Eliminates reliance on cloud inference, improving privacy and reducing latency to < 10 ms for tasks such as real‑time object detection.

Source: Arm product overview [1]

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KleidiAI – Bridging AI Models to Arm SME2

Who is KleidiAI?

• A European AI‑runtime startup founded in 2022, specializing in compiler‑backed model optimization for Arm SME2.
• Provides the KleidiAI Runtime (KRT), a lightweight C++/Rust library that maps ONNX, TensorFlow Lite, and PyTorch models directly onto SME2 primitives.

Integration workflow

1. Model import – Load an ONNX or TFLite model into KRT’s conversion tool.
2. Graph analysis – KRT identifies matrix‑friendly sub‑graphs (e.g., GEMM, conv 2D).
3. SME2 codegen – Generates platform‑specific assembly that uses SME2’s tiling instructions.
4. Binary packaging – Produces a single‑file .krt library for easy inclusion in Android APKs or iOS bundles.

Performance highlights (July 2025 benchmark suite)

Model	Latency (ms) – CPU (Arm Neoverse‑N1)	Latency (ms) – KRT + SME2	Power (mW)
MobileNet V3 (FP16)	42	12	780
EfficientDet‑D0 (FP8)	65	18	620
Whisper‑tiny (audio)	110	33	850

Source: KleidiAI press release, “SME2‑Accelerated Inference Results,” 2025.

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Real‑Time On‑Device AI Acceleration for Every smartphone

1. Faster Camera‑AI Pipelines

• Live HDR+: SME2 processes dual‑exposure frames in parallel; KRT reduces end‑to‑end latency from 48 ms to 14 ms, enabling true 60 fps HDR video.
• AI‑enhanced zoom: Super‑resolution models run on‑device, delivering 4× upscaling with < 20 ms delay, eliminating band‑width bottlenecks.

2.Clever Voice & Speech

• wake‑word detection runs on a 0.5 W SME2 slice, extending battery life by up to 15 % compared with DSP‑only solutions.
• On‑device transcription (Whisper‑tiny) delivers sub‑30 ms response for short commands, critical for AR overlays.

3. Gaming & AR

• Real‑time pose estimation for AR filters executes at 90 fps on flagship devices, thanks to hardware‑level matrix ops that avoid GPU contention.
• Physics‑aware AI (e.g., neural‑network‑based cloth simulation) runs concurrently with the graphics pipeline, leveraging SME2’s autonomous compute lanes.

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Power‑Efficiency Strategies

Strategy	Implementation Detail	Impact
Dynamic Precision Scaling	KRT monitors thermal headroom and automatically switches FP16 → FP8 when temperature > 45 °C.	Up to 30 % power reduction with < 5 % accuracy loss.
Sparse Model Execution	SME2’s mask registers skip zero weights; KRT inserts sparsity masks during graph conversion.	MAC reduction ≈ 70 % for pruned models, cutting runtime power by ~ 25 %.
Tick‑Based Clock Gating	SME2 units are gate‑controlled per tile; idle tiles are clock‑gated at sub‑microsecond granularity.	Baseline idle power < 10 mW, ideal for always‑on AI assistants.

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Advancement Tips for OEMs & App Makers

1. Profile with Arm Performance Analyzer – Capture SME2‑specific counters (SME2_TileOps, SME2_MaskHits) to identify bottlenecks.
2. Leverage KleidiAI’s auto‑tiling – Set the --max-tile-size flag to match the device’s L2 cache (typically 256 KB) for optimal data locality.
3. Hybrid Scheduling – Offload non‑matrix workloads (e.g., audio pre‑processing) to the Arm Cortex‑M55 DSP, reserving SME2 exclusively for matrix‑heavy layers.
4. Edge‑AI Security – Use Arm’s TrustZone‑enabled key store to sign .krt binaries; KRT validates signatures before runtime execution, protecting model integrity.

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Real‑World Adoption Cases

• MediaTek dimensity 9400 (2025) – First mainstream SoC to ship with Arm SME2 cores. OEMs reported a 3× speed‑up for AI camera pipelines when paired with KleidiAI’s runtime.
• Xiaomi Mi 13 Ultra (2025) – Integrated KleidiAI’s KRT for on‑device photo‑enhancement; internal tests showed a 22 % reduction in overall image‑processing time versus the previous generation.
• Google Pixel 9 (2025) – Utilized SME2 for “Live Translate” subtitles, achieving sub‑150 ms end‑to‑end latency even on low‑light video streams.

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Future Outlook: Scaling SME2 Across the Mobile Ecosystem

• Standardization – Arm’s SME2 instruction set is now part of the Arm Neoverse‑N2 baseline, paving the way for global AI acceleration in mid‑range devices.
• Toolchain evolution – LLVM 18 includes native SME2 intrinsics, simplifying manual fine‑tuning for performance‑critical kernels.
• Ecosystem expansion – KleidiAI announced a partnership with TensorFlow Lite in Q1 2026 to expose a “SME2‑accelerated” delegate, simplifying integration for developers unfamiliar with low‑level assembly.

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All data referenced is drawn from publicly available Arm product documentation and KleidiAI released performance benchmarks (july 2025).