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Computer Science > Machine Learning

arXiv:2101.07948 (cs)
[Submitted on 20 Jan 2021]

Title:SparseDNN: Fast Sparse Deep Learning Inference on CPUs

Authors:Ziheng Wang
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    Abstract: The last few years have seen gigantic leaps in
    algorithms and systems to support efficient deep learning
    inference. Pruning and quantization algorithms can now
    consistently compress neural networks by an order of magnitude.
    For a compressed neural network, a multitude of inference
    frameworks have been designed to maximize the performance of the
    target hardware. While we find mature support for quantized
    neural networks in production frameworks such as OpenVINO and
    MNN, support for pruned sparse neural networks is still lacking.
    To tackle this challenge, we present SparseDNN, a sparse deep
    learning inference engine targeting CPUs. We present both
    kernel-level optimizations with a sparse code generator to
    accelerate sparse operators and novel network-level optimizations
    catering to sparse networks. We show that our sparse code
    generator can achieve significant speedups over state-of-the-art
    sparse and dense libraries. On end-to-end benchmarks such as
    Huggingface pruneBERT, SparseDNN achieves up to 5x throughput
    improvement over dense inference with state-of-the-art OpenVINO.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2101.07948 [cs.LG]
          (or arXiv:2101.07948v1 [cs.LG] for this version)

Submission history

From: Ziheng Wang [view email]
[v1] Wed, 20 Jan 2021 03:27:35 UTC (1,841 KB)
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