Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …

In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …

Machine intelligence has become a driving factor in modern society. However, its demand
outpaces the underlying electronic technology due to limitations given by fundamental …

Recent success in deep neural networks has generated strong interest in hardware
accelerators to improve speed and energy consumption. This paper presents a new type of …

Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for
extracting features from large datasets for applications such as computer vision and natural …

Abstract Recently low-bit (eg, 8-bit) network quantization has been extensively studied to
accelerate the inference. Besides inference, low-bit training with quantized gradients can …

With the growing demand for processing higher fidelity data and the use of faster computing
cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming …

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …