Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

Transformers are slow and memory-hungry on long sequences, since the time and memory
complexity of self-attention are quadratic in sequence length. Approximate attention …

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

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

There are increasing uses of deep learning (DL) compilers to generate optimized code,
boosting the runtime performance of DL models on specific hardware. Like their traditional …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …