Inference Optimization And Latency Reduction

via “efficient inference through encoder-decoder caching”

Microsoft's unified model for diverse vision tasks.

Unique: Implements encoder-decoder caching where visual encoder output is computed once and reused across all decoder steps, reducing redundant attention computation and enabling 2-3x faster inference for variable-length outputs

vs others: More efficient than non-cached inference but with higher memory overhead than single-pass models; trade-off between latency and memory usage