We identify different fused implementations of non element-wise layers, and discuss the trade-offs between them. We demonstrate the generality of our fused ...
We identify different fused implementations of non element-wise layers, and discuss the trade-offs between them. We demonstrate the generality of our fused ...
The use of Attention Layers has become a trend since the popularization of the Transformer-based models, being the key element for many state-of-the-art models ...
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What is the fusion layer in deep learning?
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Abstract. This paper proposes layer fusion - a model compression technique that discovers which weights to combine and then fuses weights of similar ...
Mar 31, 2023 · In this paper, we propose a new Fused Depthwise Tiling (FDT) method for the memory optimization of DNNs, which, compared to existing tiling.
BibSonomy · Fusing Non Element-wise Layers in DNNs.
Sep 15, 2016 · This is a 2-layer network because it has a single hidden layer and an output layer. We don't count the first layer.
Missing: Fusing | Show results with:Fusing
These layers are usually used to map the input to another space where hopefully the problem (e.g. classification or regression) can be solved more easily. Note.
In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance.
Missing: Fusing Element-
[PDF] Accelerating DNNs Inference with Predictive Layer Fusion
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Jun 17, 2021 · In prediction mode, each 16-bit adder operates as four 4-bit adders via gating the carry chains; this allows 24 partial sums for the ternary.