One of the highlights of the Jetson software ecosystem is an incredible
deep learning toolkit built on CUDA, providing Jetson with onboard inference and the ability to apply reasoning in the field. Included is NVIDIA’s cuDNN library, adopted by multiple deep learning frameworks including Caffe.
We ran a power benchmark using the Caffe AlexNet image classifier, comparing Jetson TX1 to an Intel Core i7-6700K Skylake CPU. The table shows the results. Read more about these results in the post
“Inference: The Next Step in GPU-Accelerated Deep Learning”.
platform img / s Power (AP+DRAM) Perf/watt Efficiency versus i7-6700K
Intel i7-6700K 242 62.5W 3.88 1x
Jetson TX1 258 5.7W 45
11.5x
Kespry Designs, a Silicon Valley industrial drone developer, is using deep learning on Jetson TX1 to provide inference on construction sites for asset tracking of equipment and materials. This takes the tiresome, human-intensive work out of looking after assets and on-site logistical planning. Due to the low SWaP and computational capability of Jetson TX1, Kespry plans to migrate processing onboard Unmanned Aerial Vehicles instead of offline in the datacenter, shortening response times for tasks like inspection and triage.