ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding
While draft-model-free speculative decoding offers a promising path to efficient LLM inference, it is frequently constrained by stale draft candidates and the high computational cost of the verification. To address these challenges, we propose ECHO, a hierarchical dual-loop framework that exploits the functional asymmetry between LLM layers. Leveraging the high discriminative efficiency of early layers and the authoritative distribution of final layers, ECHO bifurcates inference into a high-frequency inner loop and a low-frequency outer loop. Within the inner loop, early-layer bonus logits dri
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- PossiblePossibly related (embedding) · 52%Free LM Studio Accelerates LLM Inference with Three Speculative Decoding Methods - finance.biggo.com →
- PossiblePossibly related (embedding) · 48%DSpark: Speculative decoding accelerates LLM inference [pdf] →
- LinkedLinked via arxiv author · 85%Ziyang Ma →
“ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding”
- LinkedLinked via arxiv author · 85%Zihong Zhang →
“ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding”
- LinkedLinked via arxiv author · 85%Zuchao Li →
“ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding”
- LinkedLinked via arxiv author · 85%Lefei Zhang →
“ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding”
- LinkedLinked via arxiv author · 85%Baoyuan Qi →
“ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding”
- LinkedLinked via arxiv author · 85%Siqi Liu →
“ECHO: Early-layer Collaborative Hierarchical Orchestration with Bonus Logits in Speculative Decoding”
