LegUp Computing closes a seed-funding round led by Intel Capital

By Andrew Canis,

TORONTO, February 22, 2018 — LegUp Computing, Inc. announced today that it closed a seed funding round led by Intel Capital. LegUp offers a cloud platform that enables software developers to program, deploy, scale, and manage FPGA devices for accelerating high performance applications without requiring hardware expertise. The technology enables the next generation of low-latency and high-throughput computing on the vast amount of real-time data processed in the cloud. LegUp Computing, Inc., was spawned from years of research in the Dept. of Electrical and Computer Engineering at the University of Toronto to commercialize the award-winning open-source LegUp high-level synthesis tool.

LegUp Computing Team

LegUp Computing team from left to right: Omar Ragheb, Zhi Li, Dr. Andrew Canis, Ruolong Lian, Dr. Jongsok Choi, and University of Toronto Professor Jason Anderson (Photo: Jessica MacInnis)

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Faster Facial Analytics using an FPGA

By Ruolong Lian,


The quality and price of image sensors has seen a huge improvement over the past decade, we are now seeing increased adoption of cameras in the automotive sector. One new application is a driver facing camera that can monitor the driver for signs of drowsiness. If the driver is about to fall asleep, we can trigger an alarm. Implementing a system like this requires a camera and an embedded processor to analyze the video stream, looking for the driver’s face and performing facial landmark detection to determine the location of their eye lids.

We have recently worked with the company Eyeris who specializes in these facial analytics software algorithms. However, they were having a problem, the software algorithms ran too slowly on an embedded processor.  They came to Efinix, a company that specializes in programmable hardware acceleration platforms, who contacted us to help them convert this facial analysis written in software into hardware that can run on an FPGA.

In the video below shows three versions of the facial analytics demo. First, running on the embedded processor (~5 frames per second), then running on an FPGA (~13 FPS), and finally on a smaller video canvas (~15 FPS). You can see that the responsiveness improves tremendously by using the FPGA to accelerate this application. Eyeris showed this demo to some of their customers during CES this year:


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