In this paper, we provide a systematic approach for assessing and comparing the
computational complexity of neural network layers in digital signal processing. We provide …

Deep neural networks are gaining in popularity as they are used to generate state-of-the-art
results for a variety of computer vision and machine learning applications. At the same time …

We survey and summarize the literature on the computational aspects of neural network
models by presenting a detailed taxonomy of the various models according to their …

Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …

As neural networks have become more powerful, there has been a rising desire to deploy
them in the real world; however, the power and accuracy of neural networks is largely due to …

Deploying Deep Neural Networks in low-power embedded devices for real time-constrained
applications requires optimization of memory and computational complexity of the networks …

Neural networks have established as a generic and powerful means to approach
challenging problems such as image classification, object detection or decision making …

This work targets the automated minimum-energy optimization of Quantized Neural
Networks (QNNs)-networks using low precision weights and activations. These networks are …

Quantized low-precision neural networks are very popular because they require less
computational resources for inference and can provide high performance, which is vital for …

The complexity of deep neural network algorithms for hardware implementation can be
much lowered by optimizing the word-length of weights and signals. Direct quantization of …