Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been
partly overcome with recent advances in machine learning and hardware design. Presently …

Abstract Model quantization helps to reduce model size and latency of deep neural
networks. Mixed precision quantization is favorable with customized hardwares supporting …

In order to deploy deep models in a computationally efficient manner, model quantization
approaches have been frequently used. In addition, as new hardware that supports various …

For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with
the simplest networks requiring the least computation, memory, and power. Quantizing …

Neural network quantization has achieved a high compression rate using a fixed low bit-
width representation of weights and activations while maintaining the accuracy of the high …

Parameter quantization in convolutional neural networks (CNNs) can help generate efficient
models with lower memory footprint and computational complexity. But, homogeneous …

Mixed-precision quantization, where a deep neural network's layers are quantized to
different precisions, offers the opportunity to optimize the trade-offs between model size …

Sparse active illumination enables precise time-of-flight depth sensing as it maximizes
signal-to-noise ratio for low power budgets. However, depth completion is required to …

In recent years, communication engineers put strong emphasis on artificial neural network
(ANN)-based algorithms with the aim of increasing the flexibility and autonomy of the system …