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 …

The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototyping to deployment. A decade later, in this article, we assess the progress …

To address one of the most challenging industry problems, we develop an enhanced
training algorithm for anomaly detection in unlabelled sequential data such as time-series …

Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

Tensor computation plays a paramount role in a broad range of domains, including machine
learning, data analytics, and scientific computing. The wide adoption of tensor computation …

In natural language processing (NLP), the" Transformer" architecture was proposed as the
first transduction model replying entirely on self-attention mechanisms without using …

Neural networks based on Long Short-Term Memory (LSTM) are widely deployed in latency-
sensitive language and speech applications. To speed up LSTM inference, previous …

Recently, significant improvement has been achieved for hardware architecture design of
deep neural networks (DNNs). However, the hardware implementation of one widely used …

Although Transformer-based language representations achieve state-of-the-art accuracy on
various natural language processing (NLP) tasks, the large model size has been …

The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …