Attend the Linley Processor Conference November 1st

Listen to Flex Logix Co-Founder Cheng Wang’s talk November 1st: A High Performance Reconfigurable Neural Accelerator with Low DRAM Bandwidth. More information HERE.

HARVARD SELECTS EFLX FOR DEEP LEARNING

 Professor Gu-Yeon Wei, Harvard University

Professor Gu-Yeon Wei, Harvard University

Harvard University is a leader in Deep Learning research.  You can see their recent ISSCC 2017 paper on their website here. 

In less than 3 months Harvard was able to integrate a 16K LUT, 2x2 array of Gen2 EFLX4K IP cores in TSMC16FFC (a mix of Logic and DSP) into their new deep learning chip which taped out in May 2017.  

2018 08 20 Harvard NN energy efficiency HotChips.png

At Hot Chips August 2018, Harvard presented the results of their first flexible DNN chip with EFLX eFPGA.

Their conclusion was that of the flexible DNN solutions, that can be reconfigured, that eFPGA is similar in area but much more energy efficient.

 

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