message: Adding a latency table for use in network architecture search.
diff: -# OVIC Benchmarker for NIPS 2018
  +# OVIC Benchmarker for CVPR 2019
  -This folder contains the SDK for track one of the [Low Power ImageNet Recognition Challenge workshop at NIPS 2018.](https://lpirc.ecn.purdue.edu/)
  +This folder contains the SDK for track one of the [Low Power ImageNet Recognition Challenge workshop at CVPR 2019.](https://lpirc.ecn.purdue.edu/)
  ## Pre-requisite
  @@ -191,3 +191,26 @@ Since Pixel 2 has excellent support for 8-bit quantized models, we strongly reco
  The detection models above are both single-shot models (i.e. no object proposal generation) using TfLite's *fast* version of Non-Max-Suppression (NMS). The fast NMS is significant faster than the regular NMS (used by the ObjectDetectionAPI in training) at the expense of about 1% mAP for the listed models.
  +
  +### Latency table
  +
  +We have compiled a latency table for common neural network operators such as
  +convolutions, separable convolutions, and matrix multiplications.
  +The table of results is available here:
  +
  +* https://storage.cloud.google.com/ovic-data/latency_table.csv
  +
  +The results were generated by creating a small network containing a single
  +operation, and running the op under the test harness. For more details see the
  +NetAdapt paper<sup>1</sup>. We plan to expand table regularly as we test with
  +newer OS releases and updates to Tensorflow Lite.
  +
  +### References
  +
  +1. **NetAdapt: Platform-Aware Neural Network Adaptation for Mobile
  + Applications**<br />
  + Yang, Tien-Ju, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler,
  + Vivienne Sze, and Hartwig Adam. In Proceedings of the European Conference
  + on Computer Vision (ECCV), pp. 285-300. 2018<br />
  + [[link]](https://arxiv.org/abs/1804.03230) arXiv:1804.03230, 2018.
  +
  