Startup Designing Hardware, Software to Propel Computer Intelligence to Next Level

Jan 30, 2018


A Purdue University-affiliated startup is designing next-generation hardware and software for deep learning aimed at enabling computers to understand the world in the same way humans do. FWDNXT, based in the Purdue Research Park, has developed a low-power mobile coprocessor called Snowflake for accelerating deep neural networks effective at image recognition and classification. Snowflake was designed with the primary goal of optimizing computational efficiency by processing multiple streams of information to mix deep learning and artificial intelligence techniques with augmented reality application.

Eugenio Culurciello, an associate professor in the Weldon School of Biomedical Engineering at Purdue, says the goal of FWDNXT is to enable computers to understand the environment so computers, phones, tablets, wearables, and robots can be helpful in daily activities. FWDNXT uses innovative algorithms to differentiate items the same ways humans do. Culurciello says Snowflake is able to achieve a computational efficiency of more than 91% on entire convolutional neural networks, which are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processing tasks. Snowflake also is able to achieve 99% efficiency on some individual layers.

FWDNXT has shown expertise in scene analysis and scene parsing, which allows the computer to perceive the outside environment. That ability is among the most difficult challenges in augmented reality content, Culurciello says.

FWDNXT’s innovation in hardware and software will be used to drive cars autonomously, to recognize faces for security and other purposes, and numerous other day-to-day purposes, such as helping people find items on their shopping lists as they walk down a store aisle or smart appliances recognizing a user’s preferences.

FWDNXT was able to create a new, efficient computer architecture with funding originally from grants it received from Purdue and the Navy, including one worth nearly $1 million.

Culurciello says FWDNXT wants to make microchips that will be used in virtually all smart device - for instance, in cars to start them, in appliances to recognize persons' preferences, in mobile phone to listen to voices.

Culurciello says FWDNXT has found a strategic partner, has obtained multimillion dollars in funding, and the next step is to seek Series A funding. FWDNXT also is looking to add to its team, which already includes Ali Zaidy, the lead architect designer of Snowflake and a deep learning expert; Abhishek Chaurasia, the team’s deep learning lead developer; Andre Chang, architect and compiler of deep learning; and Marko Vitez, neural network optimization wizard.

Another big milestone will be to develop a prototype microchip due in the first half of 2018. FWDNXT has shown it can run on FPGA, but a custom microchip would make it even more efficient.