Papers with on-device inference
ProSeqo: Projection Sequence Networks for On-Device Text Classification (D19-1)
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| Challenge: | ProSeqo is a novel on-device sequence model for text classification . it uses dynamic recurrent projections without the need to store or look up pre-trained embeddings. |
| Approach: | They propose a novel on-device sequence model for text classification using recurrent projections that uses dynamic recursion projections without the need to store or look up any pre-trained embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art neural and on-device approaches for short and long text classification tasks while maintaining low memory footprint and high accuracy. |
Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM (2026.findings-acl)
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Sravanth Kodavanti, Sowmya Vajrala, Srinivas Soumitri Miriyala, Utsav Tiwari, Uttam Kumar, Utkarsh Kumar Mahawar, Achal Pratap Singh, Arya D, Narendra Mutyala, Vikram Nelvoy Rajendiran, Sharan Kumar Allur, Euntaik Lee, Dohyoung Kim, HyeonSu Lee, Gyusung Cho, JungBae Kim
| Challenge: | a framework for efficient on-device inference of large language models is needed for smartphones . memory, latency, and runtime flexibility are constraints for large language model deployments. |
| Approach: | They propose a hardware-aware framework for efficient on-device inference of a LLaMA-based multilingual foundation model for Samsung Galaxy S24 and S25 devices with SM8650 and SM8750 chipsets respectively. |
| Outcome: | The proposed framework improves memory, latency and performance across 9 languages and 8 tasks. |