Amazon has recently announced the availability of their FPGA cloud, Amazon EC2 F1. We think that this is very exciting news, as it is the first time that FPGAs in the cloud are being available to the general public on a massive scale. This is the first step towards making FPGAs easier to use, as with the EC2 F1, a user no longer has to buy an FPGA and install it on site. FPGAs are typically much more expensive and cumbersome to buy than CPUs or GPUs, hence making them available in the cloud so that one can use them from anywhere around the world makes FPGAs much more accessible.
When the F1 instances first became available, we were excited to try them out, but we found that they were pretty difficult to use at first. Documentation was lacking (and incorrect in some case), and although Amazon provides a few examples with their AWS EC2 FPGA Hardware and Software Development Kit, the instructions to run the examples are scattered in different places and missing some steps. Understandably, they only released this service publicly in April 2017 and documentation may not have been their highest priority. To this end, we decided to write a unified guide which provides step-by-step instructions on how to run the two main examples provided by Amazon, cl_hello_world and cl_dram_dma, on the Amazon EC2 F1. This guide includes the instructions included in their AWS EC2 FPGA Hardware and Software Development Kit as well as information that we have written by trying out the examples ourselves. At the time of writing, we could not find such a step-by-step guide and we ran into issues here and there so we think that this guide will allow one to easily try out the F1 instances without getting stuck in some setup issue. So let’s dive right into it!
A paper describing the synthesis of a deep convolutional neural network (CNN) inference accelerator from C software with LegUp HLS will appear at the 2017 IEEE International System-on-Chip Conference (SOCC), at Munich, Germany, in September 2017. The work showcases the use of LegUp’s unique Pthreads flow to convert parallel software threads into spatial hardware parallelism. The accelerator incorporates a novel approach for zero-weight skipping, leveraging the ability to prune CNN weights with little inference accuracy loss.
J. H. Kim, B. Grady, R. Lian, J. Brothers, J.H. Anderson, “FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software,” IEEE SOCC, 2017 (PDF).
We are pleased to announce that LegUp 5.1 has been released!
This release is a culmination of more than 25 man-years of research and development. Prior to this release, LegUp has had 4 major releases for academic research. During these years, LegUp has been used by thousands of researchers around the world, making it the de-facto standard in high-level synthesis (HLS) research. In 2014, LegUp won the Community Award at the International Conference on Field Programmable Logic (FPL) for contributions to HLS research. LegUp has also been shown to produce state-of-the-art hardware.
We have brought all of the best features from our previous releases, and made it even better by adding new features, as well as improving the quality of the generated hardware.
New Features
Here are just some of the highlights of what we have added for this release.
LegUp IDE which provides a complete development environment with a debugger and a profiler.
Support for Xilinx, Lattice, Microsemi, and Achronix FPGAs (LegUp previously only supported Altera FPGAs).
Windows OS support (LegUp previously only supported Linux OS).
Improved pipelining.
Improved memory architecture.
Improved user messages.
Pricing
LegUp 5.1 comes with a 30-day free trial period so that you can freely try out the tool. Please note that during the trial period, you may only use LegUp for evaluation purposes only, and the generated hardware cannot be used in a product. To purchase a full license, please contact us at sales@legupcomputing.com.