These updates — NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1, and the new GPU Inference Engine (GIE) — include DIGITS 4’s new object detection workflow, one which will allow data researchers to train deep neural networks to identify and locate traffic signs, pedestrians, faces, vehicles, etc, in the mass of images.
This workflow can in turn be used for advanced autonomous driving systems, security and surveillance work, satellite imagery object identification, medical diagnostics, etc.
Of note, deep-neural network training typically involves repeated tuning of parameters by researchers in order to get high accuracy from the model, while DIGITS 4 can apparently automatically train neural networks, across a range of different tuning parameters, thereby notably lessening the time required to reach the “most” accurate solution.
The DIGITS 4 release candidate will be available as a free download this week for members of the NVIDIA developer program, reportedly.
Here’s an overview of the other two updates (cuDNN and GIE), drawing from the press release:
- NVIDIA cuDNN provides high-performance building blocks for deep learning used by all leading deep learning frameworks. Version 5.1 delivers accelerated training of deep neural networks, like University of Oxford’s VGG and Microsoft’s ResNet, which won the 2016 ImageNet challenge.
- Each new version of cuDNN has delivered performance improvements over the previous version, accelerating the latest advances in deep learning neural networks and machine learning algorithms.
- The GPU Inference Engine (GIE) is a high-performance deep learning inference solution for production environments. GIE optimizes trained deep neural networks for efficient runtime performance, delivering up to 16x better performance per watt on an NVIDIA Tesla M4 GPU vs the CPU-only systems commonly used for inference today.
- Using GIE, cloud service providers can more efficiently process images, video and other data in their hyperscale data center production environments with high throughput. Automotive manufacturers and embedded solutions providers can deploy powerful neural network models with high performance in their low-power platforms.
As noted previously, NVIDIA supplies components for Tesla Motors vehicles. How much it is working with Tesla on deep learning is unclear, though.
Have a tip for CleanTechnica? Want to advertise? Want to suggest a guest for our CleanTech Talk podcast? Contact us here.
CleanTechnica Holiday Wish Book
Our Latest EVObsession Video
CleanTechnica uses affiliate links. See our policy here.