卷積神經網絡(CNN) 已使用在許多人工智能應用中，例如物件偵測、圖像分類和自然語言處理。由於CNN 需要大量的計算資源，因此許多計算架構被提出用來提高計算的吞吐量和能效。然而，這些計算架構需要在晶片和晶片外的記憶體之間進行大量的數據移動，這會導致晶片外的記憶體的高能量消耗；因此有研究為了減少大量的數據移動而提出特徵圖壓縮。這也讓特徵圖壓縮的設計成為CNN加速器晶片外記憶體能效的主要研究之一。在這項工作中，我們提出了用於CNN加速器的浮點特徵圖壓縮器。除了壓縮零之外，我們還根據浮點數格式壓縮特徵圖中的非零值。在ILSVRC 2012 數據集上與最新技術相比此壓縮演算法實現了較低的面積和相似的壓縮率。

Convolutional neural networks (CNNs) have been deployed on many artificial intelligence applications such as object detection, image classification, and natural language processing. Since CNNsneed massive computing resources, lots of computing architectures are proposed to improve the throughput and energy efficiency of the computing. However, those computing architectures need high data movement between the chip and off-chip memories, which causes high energy consumption on the off-chip memory; thus, the feature map (fmap) compression has been discussed for reducing the data movement. Therefore, the design of fmap compression becomes one of the main researches on CNN accelerator for energy efficiency on the off-chip memory. In this work, we proposed floating-point (FP) fmap compression for hardware accelerator. In addition to the zero compression, we also compress nonzero values in the fmap based on the FP format. The compression algorithm achieves low area overhead and a similar compression ratio compared with the state-of-the-art on ILSVRC 2012 dataset.

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