infnorm

5 ms·

1.
by infnorm 9y ago
Not at this time. However, in principle it would be possible to create such bindings.
2.
by infnorm 9y ago
Quantization comes in many different forms. TensorFlow lite provides optimized kernels for 8-bit uint quantization. This specific form of evaluation is not directly supported in TensorFlow right now (though it can train such a model). We wi
3.
by infnorm 9y ago
Sounds really interested. We're excited to hear about how that goes.
4.
by infnorm 9y ago
This should be possible, but we haven't tried it. We're likely going to add a simplified target that has minimal dependencies (like no Eigen) that allows building on simple platforms.
5.
by infnorm 9y ago
The quantization is done with a special training script that is quantization aware. We will be open sourcing a mobilenet quantized training script to show how to do this soon.
6.
by infnorm 9y ago
We developed TensorFlow lite to be small enough to target really small devices that lack MMU’s like the ARM Cortex M MCU series, but we haven’t done the actual work to target those devices. That being said, we are excited when the ecosyste
7.
by infnorm 9y ago
The main TensorFlow interpreter provides a lot of functionality for larger machines like servers (e.g. Desktop GPU support and distributed support). Of course, TensorFlow lite does run on standard PCs and servers, so using it on non-mobile&
8.
by infnorm 9y ago
TensorFlow Lite is an interpreter in contrast with XLA which is a compiler. The advantage of TensorFlow lite is that a single interpreter can handle several models rather than needing specialized code for each model and each target platfor