  Convolutional neural networks (CNNs) achieve remarkable performance in a wide
range of fields. However, intensive memory access of activations introduces
considerable energy consumption, impeding deployment of CNNs on
resourceconstrained edge devices. Existing works in activation compression
propose to transform feature maps for higher compressibility, thus enabling
dimension reduction. Nevertheless, in the case of aggressive dimension
reduction, these methods lead to severe accuracy drop. To improve the trade-off
between classification accuracy and compression ratio, we propose a
compression-aware projection system, which employs a learnable projection to
compensate for the reconstruction loss. In addition, a greedy selection metric
is introduced to optimize the layer-wise compression ratio allocation by
considering both accuracy and #bits reduction simultaneously. Our test results
show that the proposed methods effectively reduce 2.91x~5.97x memory access
with negligible accuracy drop on MobileNetV2/ResNet18/VGG16.
