FLAME models layer-wise overlapping parallelism and asynchronous CPU-GPU pipeline bubbles to estimate inference latency across frequencies with sparse profiling and low error for DNNs and SLMs.
Minimizing gpu kernel launch overhead in deep learning inference on mobile gpus
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge
FLAME models layer-wise overlapping parallelism and asynchronous CPU-GPU pipeline bubbles to estimate inference latency across frequencies with sparse profiling and low error for DNNs and SLMs.