In contrast to the standard dense representations used in most deep learning networks, we created networks that borrow several aspects of the brain’s efficient structure. These brain-inspired, optimized networks not only deliver equivalent accuracy to their standard counterparts, they drastically reduce computational requirements and can run on today’s hardware.
We demonstrated these performance improvements on inference tasks using the Google Speech Commands (GSC) dataset. We created optimized networks on two off-the-shelf Xilinx products:
- Alveo™ U250 – a powerful platform designed for datacenters
- Zynq™ UltraScale+ ZU3EG – a smaller platform designed for embedded applications