In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Due to recent advances in digital technologies, and availability of credible data, an area of
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …

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

Recently, transformer has achieved remarkable performance on a variety of computer vision
applications. Compared with mainstream convolutional neural networks, vision transformers …

Deep networks run with low precision operations at inference time offer power and space
advantages over high precision alternatives, but need to overcome the challenge of …

Research has shown that convolutional neural networks contain significant redundancy, and
high classification accuracy can be obtained even when weights and activations are …

Quantization can improve the execution latency and energy efficiency of neural networks on
both commodity GPUs and specialized accelerators. The majority of existing literature …

We introduce a new family of physics-inspired generative models termed PFGM++ that
unifies diffusion models and Poisson Flow Generative Models (PFGM). These models …

Real-time application of deep learning algorithms is often hindered by high computational
complexity and frequent memory accesses. Network pruning is a promising technique to …

Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently
employed in popular efficient architectures can also result in negative activation values, with …