Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Deep learning, the fastest growing segment of Artificial Neural Network (ANN), has led to the
emergence of many machine learning applications and their implementation across multiple …

Deep Neural Networks (DNNs) have reinvigorated real-world applications that rely on
learning patterns of data and are permeating into different industries and markets. Cloud …

With reduced data reuse and parallelism, recent convolutional neural networks (CNNs)
create new challenges for FPGA acceleration. Systolic arrays (SAs) are efficient, scalable …

Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural
Language Processing models. This attention mechanism calculates a correlation score for …

Convolutional Neural Networks--(CNNs) based algorithms have been successful in solving
image recognition problems, showing very large accuracy improvement. In recent years …

Abstract Generative Adversarial Network (GAN) requires extensive computing resources
making its implementation in edge devices with conventional microprocessor hardware a …

In this article, we design the first full software/hardware stack, called Uni-OPU, for an efficient
uniform hardware acceleration of different types of transposed convolutional (TCONV) …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations is …

This article presents generative adversarial network processing unit (GANPU), an energy-
efficient multiple deep neural network (DNN) training processor for GANs. It enables on …