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

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 …

There is an increasing need to bring machine learning to a wide diversity of hardware
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …

Performance is a critical challenge in mobile image processing. Given a reference imaging
pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements …

We present a new algorithm to automatically schedule Halide programs for high-
performance image processing and deep learning. We significantly improve upon the …

Most modern commodity imaging systems we use directly for photography—or indirectly rely
on for downstream applications—employ optical systems of multiple lenses that must …

Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for
improved performance and energy efficiency. Unfortunately, adoption of these architectures …

Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive the current
popularity and utility of deep learning. However, these frameworks are optimized for a …

The Halide image processing language has proven to be an effective system for authoring
high-performance image processing code. Halide programmers need only provide a high …

Next-generation information technologies will process unprecedented amounts of loosely
structured data that overwhelm existing computing systems. N3XT improves the energy …