TRACE: Efficient Transformer Residual Activation Encoding for Distributed Edge Inference
In brief
- TRACE compresses Transformer residual activations to reduce latency in distributed edge inference.
- Enables compute-communication overlap and dynamic bitrate adaptation.
- Up to 1.27x speedup and 22,624x reduction in communication overhead.
TRACE introduces a residual activation compression method for Transformers to cut latency in distributed edge inference. The approach enables compute-communication overlap and dynamic bitrate adaptation, improving both speed and accuracy over existing solutions.
What it changes for you
- Industry & ManufacturingOptimization of embedded AI models for decentralized manufacturing pipelines.
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