If a web-cam is available ET1’s code will try to automatically sync and if you can wave and point, this radial menu is a fun way to augment your ETL development. Using computer vision (explained in depth below) we map a skeleton over your hands and able to create a radial menu we are calling […]| Dev3lop
An end-to-end open source machine learning platform for everyone. Discover TensorFlow's flexible ecosystem of tools, libraries and community resources.| TensorFlow
We started this series with a look at operators and kernels, the “instructions” used by models and the implementation of those instructions on the available hardware. We then explored the computation graph, which defines the sequence of operators for a given model, and explored how different model formats opt to include the explicit computation graph in the distributed file, or defer it to the inference application. With tflite-micro and the .| danielmangum.com
In our last post we explored operators and kernels in Tensorflow Lite, and how the ability to swap out kernels depending on the hardware capabilities available can lead to dramatic performance improvements when performing inference. We made an analogy of operators to instruction set architectures (ISAs), and kernels to the hardware implementation of instructions in a processor. Just like in traditional computer programs, the sequence of instructions in a model needs to be encoded and distribu...| danielmangum.com
The buzz around “edge AI”, which means something slightly different to almost everyone you talk to, is well past reaching a fever pitch. Regardless of what edge AI means to you, the one commonality is typically that the hardware on which inference is being performed is constrained in one or more dimensions, whether it be compute, memory, or network bandwidth. Perhaps the most constrained of these platforms are microcontrollers. I have found that, while there is much discourse around “ru...| danielmangum.com
