14.5 Eyeriss: An energy efficient reconfigurable accelerator for deep convolutional neural networks
Deep learning using convolutional neural networks (CNN) gives state of the art accuracy on many computer vision tasks (e.g. object detection, recognition, segmentation) Convolutions account for over 90% of the processing in CNNs for both inference/testing and training, and fully convolutional networks are increasingly being used. To achieve state of the art accuracy requires CNNs with not only a larger number of layers, but also millions of filters weights, and varying shapes (i.e. filter sizes, number of filters, number of channels) as shown in Fig. 14.5.1. For instance, AlexNet [1] uses 2.3 million weights (4.6MB of storage) and requires 666 million MACs per 227x227 image (13kMACs/pixel) VGG16 [2] uses 14.7 million weights (29.4MB of storage) and requires 15.3 billion MACs per 224x224 image (306kMACs/pixel) The large number of filter weights and channels results in substantial data movement, which consumes significant energy.
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