GTC 2017: San Jose CA May 08, 2017

Blue Horizon Deep Learning comes to GTC 2017 in Silicon Valley




Blue Horizon, a Decentralized Autonomous Edge Compute system, presents cognitive workloads at the Edge using GPU-supported deep learning.  The Blue Horizon project has emphasized support for Cognitive microservices at the Edge, supporting devices from Raspberry Pi's, Jetson TX1/2, x86, and cloud devices such as Power8 and Minsky.


 Blue Horizon Cube

On display at GTC 2017 was Blue Horizon's Deep Learning cube, with cognitive and traditional workloads:

  • Audio Deep Learning on a Raspberry Pi 3 using a lightweight TensorFlow NN
    • Inference at the Edge: Music, Speech classification
    • Training in the Cloud
    • Continuous training via active learning with at edge stream selection of useful training data
  • Video Deep Learning on a Jetson TX2
    • Motion identification using OpenCV
    • Prioritization of images of interest: Histogram-based sorting / patch size / temporal priority
    • Inference at the Edge: Caffe NN for image classification
  • HA Microservices for lossless data transmission
    • PMU / Synchrophasor data used as an example
    • Supports US power grid throughput, latency, and high-availability needs