A collaborative inference framework combining offline DAG partitioning with online semantic-cache-based early exit and adaptive quantization achieves large latency and throughput gains in experiments, but with unverified accuracy and no released code.
Cloud computing: issues and challenges,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.DC 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization
A collaborative inference framework combining offline DAG partitioning with online semantic-cache-based early exit and adaptive quantization achieves large latency and throughput gains in experiments, but with unverified accuracy and no released code.