Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks

29 Feb 2020 Wu Chen Wang Mingyu Li Xiayu Lu Jicheng Wang Kun He Lei

Convolutional neural networks (CNNs) achieve state-of-the-art performance at the cost of becoming deeper and larger. Although quantization (both fixed-point and floating-point) has proven effective for reducing storage and memory access, two challenges -- 1) accuracy loss caused by quantization without calibration, fine-tuning or re-training for deep CNNs and 2) hardware inefficiency caused by floating-point quantization -- prevent processors from completely leveraging the benefits... (read more)

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