Call for Papers

The rapid evolution of Deep Learning, propelled by transformer-based architectures and significant hardware advancements, has unlocked unprecedented capabilities across diverse domains, from biological sciences to autonomous systems. As foundation models continue to scale, they introduce new challenges in resource management, particularly in data centers, prompting us to broaden our exploration of leveraging distributed and on-device resources for training and inference. Small Language Models (SLMs) are emerging as a compelling alternative for generative AI, offering a sustainable balance between efficiency and user privacy. This workshop aims to bring together algorithms and systems experts to discuss the opportunities and challenges of on-device machine learning. We hope to explore to what extent SLMs can compete with LLMs and identify methods to enhance their quality and efficiency. Addressing this shift requires innovation in algorithm and system co-design, underscoring the importance of interdisciplinary approaches for future applications.

To this end, we look forward to welcoming contributions in the following research areas:

• Efficient on-device training and deployment of ML
• Efficient model architectures
• On-device benchmarking and performance measurements of ML models
• Efficient ML methods for edge/mobile deployment, including but not limited to quantization, pruning, low-rank decomposition, etc.
• Approximate inference methods
• Distillation and model compression methods
• Sample-efficient learning algorithms
• AutoML methods for efficient models
• Hardware/Software co-design
• Dynamic networks, including but not limited to early-exit networks, MoE, SlimNets, path-way models, etc.
• Speculative and Parallel execution/decoding
• Zeroth-order optimization methods
• Distributed/Collaborative learning
• Federated Learning (esp. in the cross-device setting)
• Efficient Retrieval Augmented Generation (RAG) and LLM pipelines
• System performance heterogeneity
• Emergent applications for on-device ML
• Sustainable AI

Submissions

Any questions regarding submission issues should be directed to: Stefanos Laskaridis, Samuel Horvath, Berivan Isik or Peter Kairouz.

Important Dates