World Congress 2025 Aug 20, 2025 Session details

Mobile AI Just Got Faster: What’s Coming for Developers on Arm

Gian Marco Iodice

Run heavy generative AI entirely on-device without rewriting your code. Arm's KleidiAI integrates natively into standard frameworks, unlocking a 6x speedup for seamless, offline mobile execution.

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#1 about 3 min

Generative AI use cases on mobile devices

How on-device generative AI works without internet access for tasks like group chat summarization.

#2 about 2 min

Running audio generation locally on smartphones

Overcoming cloud latency by generating high-quality stereophonic audio directly on a mobile CPU using AudioGen.

#3 about 4 min

Scalability, security, and performance of Arm processors

The primary benefits of deploying AI workloads on mobile CPUs including optimization scaling and security.

#4 about 3 min

Open-source community and machine learning frameworks

Leveraging open-source frameworks like ExecuTorch and ONNX Runtime for diverse AI model deployments.

#5 about 3 min

Optimizing AI routines with the KleidiAI library

Integrating a lightweight C-based micro-kernel library into popular frameworks to accelerate neural networks.

#6 about 4 min

AudioGen pipelines and mixed memory data types

How on-device generative AI reduces cloud latency for iterative audio production using flexible floating-point operations.

#7 about 3 min

Building private smart assistants without cloud dependencies

Running speech-to-text, large language models, and text-to-speech stages securely on-device without internet connectivity.

#8 about 4 min

Matrix multiplication with SME2 architecture instructions

Using the Scalable Vector Extension 2 and Matrix Outer Product Accumulate to speed up heavy computations.

#9 about 3 min

Performance benchmarks and Android developer adoption

Unlocking significant performance speedups on key AI models and preparing Android applications for automatic hardware acceleration.

Matching moments

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Debunking common biases about limited mobile AI capabilities

Sasha Denisov Sasha Denisov · World Congress 2026 Europe

3:42 min

Accelerating machine learning workloads using KleidiAI libraries

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3:21 min

Reducing cloud dependency with on-device edge AI models

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48 sec

Shifting artificial intelligence models to local smartphone hardware

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Addressing mobile energy limits and AI processing tradeoffs

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3:38 min

The convergence of mobile engineering and machine learning

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