Enhancing Generative AI Performance and Efficiency Across Di
Generative AI (GenAI) is rapidly transitioning from high-end flagship smartphones and specialized hardware to a broader spectrum of devices, including those several years old and low-power edge platforms like the Raspberry Pi 5. This shift is largely enabled by advances in Arm CPU architectures and software optimizations such as ExecuTorch 0.7, which now integrates KleidiAI by default to provide automatic AI acceleration without any integration overhead for developers.
Arm’s SDOT (Signed Dot Product) instruction, introduced in the Armv8.2 architecture, has become a critical enabler for efficient low-bit precision matrix multiplications central to large language models (LLMs). This instruction accelerates core computations on 8-bit signed integer vectors, allowing complex GenAI workloads to run smoothly even on devices up to five years old, expanding the potential user base to billions of existing smartphones in the context of Generative AI, especially regarding Arm-based devices in the context of AI model acceleration, including Arm-based devices applications. The seamless embedding of KleidiAI into popular Edge AI frameworks such as XNNPack, MediaPipe, MNN, ONNX Runtime, and llama.cpp creates a turnkey solution for AI model acceleration.
Developers can now benefit from faster model startups, reduced latency, and lower memory consumption without manual tuning or architecture-specific code modifications. These performance gains translate directly into practical on-device applications, such as private voice assistants, message summarization, and local AI copilots that operate without cloud dependency or excessive energy consumption.
For example, running a quantized Llama 3.2 1B model on Arm CPUs with KleidiAI and ExecuTorch achieves over 20% improvement in prefill phase throughput on devices like the Samsung Galaxy S24+, enabling real-time tasks like summarizing around 50 unread messages with a smooth user experience in the context of Generative AI in the context of Arm-based devices, particularly in AI model acceleration. With these advances, the industry is witnessing a democratization of GenAI capabilities. Devices that once lacked the computational power for efficient LLM inference can now support meaningful AI tasks locally.
This shift not only enhances user privacy by eliminating the need to offload data to the cloud but also opens the door to innovative applications such as context-aware autocomplete in offline text editors and fully private smart assistants powered by local speech-to – text and text-to – speech models. The combination of SDOT-enabled Arm CPUs, KleidiAI acceleration, and the ExecuTorch runtime is a pivotal step toward making GenAI accessible on billions of devices, reducing the digital divide inherent in AI technology deployment.
Enhancing Large Language Model Encoder Efficiency Using Nest
While hardware acceleration is vital, software-level optimizations in model architecture and data representation play an equally critical role in scaling large language model deployments. One notable bottleneck in LLM-based encoders lies in handling variable-length input sequences efficiently.
Traditional approaches pad all sequences in a batch to a fixed length, which results in wasted computation and memory when many sequences are shorter than the maximum length. Nested Jagged Tensors (NJTs) in PyTorch provide an elegant solution by offering a packed, contiguous memory layout that natively supports ragged-shaped data, enabling batches of variable-length sequences to be processed without padding overhead. The DRAMA dense retrieval model, built upon a pruned LLaMA backbone, exemplifies the practical benefits of NJTs.
Despite its modest size—0.1B non-embedding parameters in the base version—DRAMA delivers strong retrieval accuracy across both English and multilingual datasets in the context of Generative AI in the context of Arm-based devices in the context of AI model acceleration, especially regarding Generative AI in the context of Arm-based devices, including AI model acceleration applications. However, its production deployment was previously hindered by computational inefficiencies.
By refactoring DRAMA to use NJTs for sequence representation, inference throughput improved by 1.7x to 2.3x, significantly enhancing its production readiness. The performance gains come from NJTs’ ability to avoid unnecessary computation on padding tokens, which is especially impactful in batches with heterogeneous sequence lengths. Benchmarks demonstrate that NJTs outperform padded tensor implementations by up to 1, especially regarding Generative AI, including Arm-based devices applications, including AI model acceleration applications.85x in scenarios with linearly increasing sequence lengths and even more in cases with outlier sequences.
This efficiency is achieved by modifying key model components such as the token embedding transformation and attention mechanisms to operate on jagged tensors without masks, reducing overhead and maximizing hardware utilization. Implementing NJTs requires careful adaptation; for example, the transform module converts token IDs into jagged token IDs, eliminating the need for attention masks.
Additionally, attention layers like LlamaSdpaAttention incorporate specialized functions such as repeat_kv to handle grouped query attention efficiently within the jagged tensor paradigm. While NJTs currently support a single ragged dimension and introduce some Python-level overhead, compiling NJT operations and operator fusion can mitigate these costs, making NJTs a valuable tool for optimizing LLM inference on production hardware.
Integrating Hardware and Software Advances for Scalable On –
The convergence of hardware features and software techniques is crucial to overcoming the inherent challenges of deploying large language models on resource-constrained devices. Arm’s CPU innovations, such as the SDOT instruction and I8MM capabilities, combined with software acceleration layers like KleidiAI and runtime frameworks like ExecuTorch, provide a robust foundation for efficient on-device GenAI.
These advancements enable complex models like Llama 3.2 to run with low latency and manageable power consumption on a wide range of devices, not just the latest high-end smartphones. Simultaneously, software-level enhancements like Nested Jagged Tensors address sequence length variability, a common source of inefficiency in practical workloads. By avoiding padding overhead, NJTs enable more efficient memory and compute resource usage, which is essential when handling real-world data that often exhibits diverse input lengths, particularly in Generative AI, particularly in Arm-based devices, including AI model acceleration applications, including Generative AI applications in the context of AI model acceleration.
This synergy of hardware and software optimization unlocks new opportunities for application developers to build rich, privacy-conscious AI experiences that function entirely offline. Key benefits of this integrated approach include: ① On-device private voice assistants that maintain user data confidentiality by eliminating cloud dependencies.
② Real-time message summarization and text completion that improve productivity without latency bottlenecks.
③ Context-aware code and text editing copilots that provide intelligent suggestions seamlessly within local environments.
④ Deployment feasibility on billions of Arm-based devices in circulation, significantly broadening the reach of GenAI technologies, especially regarding Generative AI, particularly in Arm-based devices, particularly in AI model acceleration. This holistic strategy not only reduces the technical barriers for AI adoption but also aligns with evolving data privacy regulations and consumer expectations for secure, responsive applications.
Developers and organizations are encouraged to leverage resources like Arm’s learning paths and open-source frameworks to begin integrating these innovations into their AI pipelines. In conclusion, the combined impact of Arm’s CPU instruction set extensions, KleidiAI acceleration, ExecuTorch runtime, and PyTorch Nested Jagged Tensors marks a transformative phase in AI deployment. Together, they enable scalable, efficient, and privacy-preserving GenAI experiences across a diverse spectrum of devices, fostering inclusive access to the benefits of artificial intelligence.
