The extraction and proper utilization of convolution neural network (CNN) features have a significant impact on the performance of image super-resolution (SR). Although CNN features contain both the spatial and channel information, current deep techniques on SR often suffer to maximize performance due to using either the spatial or channel information. Moreover, they integrate such information within a deep or wide network rather than exploiting all the available features, eventually resulting in high computational complexity. To address these issues, we present a binarized feature fusion (BFF) structure that utilizes the extracted features from residual groups (RG) in an effective way. Each residual group (RG) consists of multiple hybrid residual attention blocks (HRAB) that effectively integrates the multiscale feature extraction module and channel attention mechanism in a single block. Furthermore, we use dilated convolutions with different dilation factors to extract multiscale features. We also propose to adopt global, short and long skip connections and residual groups (RG) structure to ease the flow of information without losing important features details. In the paper, we call this overall network architecture as hybrid residual attention network (HRAN). In the experiment, we have observed the efficacy of our method against the state-of-the-art methods for both the quantitative and qualitative comparisons.

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Deep Learning techniques have been successfully used to solve a wide range of computer vision problems. Due to their high computation complexity, specialized hardware accelerators are being proposed to achieve high performance and efficiency for deep learning-based algorithms. However, soft errors, i.e., bit flipping errors in the layer output, are often caused due to process variation and high energy particles in these hardware systems. These can significantly reduce model accuracy. To remedy this problem, we propose new algorithms that effectively reduce the impact of errors, thus keeping high accuracy. We firstly propose to incorporate an Error Correction Layer (ECL) into neural networks where convolution is performed multiple times in each layer and majority reporting is conducted for the outputs at bit level. We found that ECL can eliminate most errors while bypassing the bit-error when the bits at the same position are corrupted multiple times under the simulated condition. In order to solve this problem, we analyze the impact of errors depending on the position of bits, thus observing that errors in most significant bit (MSB) positions tend to severely corrupt the output of the network compared to the errors in the least significant bit (LSB) positions. According to this observation, we propose a new specialized activation function, called Piece-wise Rectified Linear Unit (PwReLU), which selectively suppresses errors depending on the bit positions, resulting in an increased model resistance against the errors. Compared to existing activation functions, the proposed PwReLU outperforms with large accuracy margins of up-to 20% even with very high bit error rates (BERs). Our extensive experiments show that the proposed ECL and PwReLU work in a complementary manner, achieving comparable accuracy to the error-free networks even at a severe BER of 0.1% on CIFAR10, CIFAR100, and ImageNet.