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In this paper, we propose a new acceleration method called Direct Conversion that considers the weight sparsity under the sparse input activation condition.
In this paper, we propose a new acceleration method called Direct Conversion that considers the weight sparsity under the sparse input activation condition. The ...
A new acceleration method called Direct Conversion is proposed that considers the weight sparsity under the sparse input activation condition and converts a ...
In this paper, we propose a new acceleration method called Direct Conversion that considers the weight sparsity under the sparse input activation condition. The ...
Request PDF | On Oct 18, 2020, Won-Hyuk Lee and others published Direct Conversion: Accelerating Convolutional Neural Networks Utilizing Sparse Input Activation
Spartan is a lightweight hardware/software framework for accelerating DNN training on GPUs [22], which can exploit activation sparsity detected during training.
Accelerating a model can also be achieved not only by zero-skipping of weights but also by zero-skipping input activation maps, a fact widely used in neural ...
Missing: Conversion: | Show results with:Conversion:
Jul 3, 2024 · This paper introduces a simple and effective sparsification method named “ProSparse” to push LLMs for higher activation sparsity while maintaining comparable ...
May 25, 2022 · This article proposes a novel hardware accelerator for the inference task with sparse convolutional neural networks (CNNs)
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This paper proposes a novel hardware accelerator for inference of sparse convolutional neural networks (CNNs) by building a hardware unit to perform Image to ...