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Abstract: Quantization is an effective technique to reduce memory and computational costs for inference of convolutional neural networks (CNNs).
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Sep 25, 2019 · This paper reveals that "prune-then-quantize method" is the best strategy to achieve Pareto-optimal performance by using a proposed ...
In this paper, we use MAC×bit not only to simply evaluate the computational cost but also as a regularization method. ... Accelerating CNN Inference with an ...
Sep 25, 2019 · A hardwareagnostic metric for measuring computational costs is proposed and it is demonstrated that Pareto-optimal performance is achieved ...
To provide optimal accuracy in a low computational- cost region, we apply the proposed prune-then-quantize method to the post-training quantization scenario.
Paper Information: Paper Title: Quantization Strategy for Pareto-Optimally Low-Cost and Accurate CNN. Student Contest: No. Affiliation Type: Industry.
Missing: optimality | Show results with:optimality
<http://purl.org/dc/terms/title>. "prune or quantize strategy for pareto optimally low cost and accurate cnn ... pareto-optimal>. 13. <http://aida.kmi.open.ac.uk ...
Owing to retraining, QAT is able to quantize CNN models in low precision representation without noticeable accuracy drop and can even operate at 2 bits. However ...
Apr 8, 2024 · CNNs utilizing quantized weights and activations and suitable mappings can significantly improve trade-offs among the accuracy, energy, and ...
In this work, we use ResNet as a case study to systematically investigate the effects of quan- tization on inference compute cost-quality tradeoff curves. Our ...
Missing: CNN. | Show results with:CNN.