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 …

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

Neural radiance fields enable state-of-the-art photorealistic view synthesis. However,
existing radiance field representations are either too compute-intensive for real-time …

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

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Vision transformers (ViTs) have recently received explosive popularity, but their enormous
model sizes and training costs remain daunting. Conventional post-training pruning often …

Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Abstract The growth of Graph Convolution Network (GCN) model sizes has revolutionized
numerous applications, surpassing human performance in areas such as personal …

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients'
data privacy and security issues. Private inference (PI) techniques using cryptographic …

Network quantization aims at reducing bit-widths of weights and/or activations, particularly
important for implementing deep neural networks with limited hardware resources. Most …