![]() ![]() We will use Vitis for building the embedded application. Make sure to install Vitis, which will include Vivado and Vitis. We will be using Vivado 2021.1, which you can download and use for free for educational projects. We will demonstrate that running the CIFAR test data set shows very little accuracy drop when rounding down from the original 32-bit floating point. Importantly, we still won’t quantize the model and use Tensil with 16-bit fixed point data type. To do this, we will use the powerful ZCU104 board and implement an embedded application to remove the overhead of running Linux OS and PYNQ. In this tutorial we will show how to optimize Tensil running ResNet20 trained on CIFAR for maximum performance. Sometimes the application requires pushing the performance to its limits. Getting Tensil to run ResNet at 300 frames per second on ZCU104 - K155LA3įPGA Getting Tensil to run ResNet at 300 frames per second on ZCU104 ![]()
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