Enhancing On-Device AI: Advanced Quantization and Hardware-Software Co-Design
If you crave powerful on-device AI that is efficient without draining your memory budget or making your device overheat, you need tools that go beyond basic post-training quantization. This approach often falls short, as it compromises accuracy without recovering any lost performance. To truly unleash the potential of edge AI on devices, a more refined strategy is essential.
- Practical Jupyter Notebook Tutorials for Developers
- The Essence of Quantization and Hardware-Software Co-Design
- Optimized Performance with Arm’s KleidiAI
- Crafting Loss Functions for Effective Training
- Exploring Extreme Quantization Techniques
- Leveraging Mixture of Experts Architectures
- A Collaborative Initiative
- Further Reading
Practical Jupyter Notebook Tutorials for Developers
To assist developers and machine learning (ML) researchers, we’ve crafted a series of practical Jupyter notebook tutorials. These resources introduce a variety of advanced topics in hardware-software co-design, illustrating how techniques like mixed-precision quantization, quantization-aware methods, and mixtures of expert models can produce efficient, compact, and capable AI models. Our focus is on preparing these models to run seamlessly on Arm-based devices and edge inference runtimes such as ExecuTorch.
The Essence of Quantization and Hardware-Software Co-Design
Quantization aims to fine-tune precision to minimize accuracy loss while maximizing model compression. Traditional methods, transitioning from FP32 to INT8, serve as powerful but blunt instruments. The challenge lies in understanding that not all layers in a neural network exhibit the same sensitivity to precision loss. Optimizing the precision required depends on the distribution of your data.
For instance, consider how at 4-bit quantization different components within a transformer architecture—like feedforward and attention layers—experience varying levels of quantization error. Our approach advocates for adaptive bit allocation, ensuring each segment of the network is represented with the appropriate precision. This can be easily implemented in PyTorch using the QConfig API.
Optimized Performance with Arm’s KleidiAI
Another critical advancement is Arm’s KleidiAI, which provides highly optimized compute kernels down to 4-bit precision. This optimization ensures that low-bit tensor types are efficiently mapped to Arm hardware instructions. For developers targeting Arm-based devices, this seamless integration occurs via PyTorch and the ExecuTorch runtime, utilizing the KleidiAI and Arm VGF backends.
More Read
Our tutorials delve into hardware-software co-design, where we not only minimize loss but also streamline model size by teaching the model to quantize each layer effectively. This balance between accuracy and compactness allows developers to create models that consistently fit within defined memory constraints.
Crafting Loss Functions for Effective Training
One innovative approach we explore is forming a loss function that accounts for both software costs—model accuracy—and hardware costs—static model size. We implement this in PyTorch and elaborate on its application in our Hardware–Software Co-Design tutorials. One example involves training a transformer model on the Tiny Shakespeare dataset, where we can balance performance against model compactness.
Exploring Extreme Quantization Techniques
Building upon the co-design philosophy, our tutorials address training algorithms that facilitate aggressive low-bit deployments. Quantization-aware training (QAT) simulates low-precision arithmetic during the training phase, allowing the model to adapt its weights and activations in preparation for rounding noise. By incorporating quantization early in the training process, the optimizer effectively anticipates the quantizer’s behavior, which proves especially beneficial for ultra-low-bit targets.
Extreme quantization challenges us to explore how close we can get to binary-like representations while retaining functional accuracy. Research, such as “The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits” (Ma et al., 2024), shows how algorithm-hardware co-design can compress modern architectures significantly without sacrificing functionality.
Our Jupyter notebooks provide hands-on opportunities to experiment with these concepts in real-time. Starting from a baseline model, you can enable QAT, explore various quantization schedules, and assess the trade-offs between accuracy, model size, and performance.
Leveraging Mixture of Experts Architectures
Beyond quantization, our curriculum offers an introductory look at Mixture of Experts (MoE) models. Unlike dense models where every parameter is active for every input, MoE architectures activate only a subset of the network—referred to as “experts”—for any given input token. This targeted activation enhances model efficiency while preserving accuracy.
To facilitate learning about these advanced topics, we’ve released a comprehensive series of Jupyter Notebooks that serve as a practical step-by-step guide. With approximately 10 hours of actionable content, these labs allow you to run and modify code directly on your hardware.
A Collaborative Initiative
This collection is the result of collaborative efforts among specialists including Kieran Hejmadi at Arm, Oliver Grainge, an AI researcher from the University of Southampton, and Professor Constantine Caramanis, IEEE Fellow from the University of Texas at Austin. We also acknowledge the contributions of academic reviewers from IIT Delhi and IIT Hyderabad, ensuring that our material is both cutting-edge and rigorously validated.
For those interested in more foundational content, we recommend our course on Optimizing GenAI on Arm Processors, from Edge to Cloud.
Further Reading
- Ma, S., et al. (2024). The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. arXiv
Together, these insights enable developers and researchers to harness the full potential of on-device AI while keeping efficiency and performance at the forefront.
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