Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

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 have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Cross-silo federated learning (FL) enables organizations (eg, financial, or medical) to
collaboratively train a machine learning model by aggregating local gradient updates from …

Hardware-friendly network quantization (eg, binary/uniform quantization) can efficiently
accelerate the inference and meanwhile reduce memory consumption of the deep neural …

Quantization techniques can reduce the size of Deep Neural Networks and improve
inference latency and throughput by taking advantage of high throughput integer …

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are
both binarized into 1-bit values, thus greatly reducing the memory usage and computational …

Quantization plays an important role in the energy-efficient deployment of deep neural
networks on resource-limited devices. Post-training quantization is highly desirable since it …

This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …