In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

This study presents a novel autonomous vision-based bolt-looseness detection method for
splice bolted connections. The method is sequentially designed with a Faster regional …

With the slowing of Moore's law, computer architects have turned to domain-specific
hardware specialization to continue improving the performance and efficiency of computing …

A spatial accelerator's efficiency depends heavily on both its mapper and cost models to
generate optimized map**s for various operators of DNN models. However, existing cost …

Image processing and machine learning applications benefit tremendously from hardware
acceleration. Existing compilers target either FPGAs, which sacrifice power and performance …

Resource-efficient Convolutional Neural Networks (CNNs) are gaining more attention.
These CNNs have relatively low computational and memory requirements. A common …

To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it
is necessary to compress the CNN models by performing quantization, whereby precision …

We present the first open-source TensorFlow to FPGA tool capable of running state-of-the-
art DNNs. Running TensorFlow on the Amazon cloud FPGA instances, we provide …

Seeking the “sweet spot” in the accuracy-efficiency trade-off is increasing the heterogeneity
of state-of-the-art Convolutional Neural Networks (CNNs). Such CNN models exhibit …

In recent years, heterogeneous computing has emerged as the vital way to increase
computers? performance and energy efficiency by combining diverse hardware devices …