DEFA introduces a novel method, combining frequency-weighted pruning and probability-aware pruning to reduce memory footprint by over 80%. It explores multi-scale parallelism to boost throughput significantly. Extensively evaluated, DEFA achieves substantial speedup and energy efficiency improvements compared to GPUs. The architecture utilizes fine-grained layer fusion and feature map reusing to further reduce memory access. DEFA's approach addresses the inefficiencies of MSDeformAttn on general-purpose platforms like CPUs and GPUs. It provides pioneering support for MSDeformAttn in object detection tasks.
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by Yansong Xu,D... klo arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.10913.pdfSyvällisempiä Kysymyksiä