Papers with edge computing
An Empirical Study of Speculative Decoding for Small Language Models (2026.eacl-long)
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| Challenge: | Existing studies focus on 7B-70B parameters models, leaving a knowledge gap for small language models. |
| Approach: | They propose a draft-then-verify paradigm that allows for a single forward pass through a model and transfer of all model parameters to the GPU cache. |
| Outcome: | The proposed method can be used to accelerate small language models with low computational overhead. |
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning (2020.findings-emnlp)
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| Challenge: | Pretrained large-scale language models have been criticized for their limited weight storage and computational speed on hardware platforms. |
| Approach: | They propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning. |
| Outcome: | The proposed model achieves 5.0x accuracy on GLUE benchmarks and 1.79x compression rate on DistilBERT. |
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)
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| Challenge: | Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning. |
| Approach: | They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations. |
| Outcome: | The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs. |