Industrial revolutions, deep learning & NVIDIA’s 3U AI super computer @ FMS 2017

I was at Flash Memory Summit this past week and besides the fire on the exhibit floor, there was a interesting keynote by Andy Steinbach, PhD from NVIDIA on “Deep Learning: Extracting Maximum Knowledge from Big Data using Big Compute”.  The title was a bit much but his session was great.

2012 the dawn of the 4th industrial revolution

Steinbach started off describing AI, machine learning and deep learning as another industrial revolution, similar to the emergence of steam engines, mass production and automation of production. All of which have changed the world for the better.

Steinbach said that AI is been gestating for 50 years now but in 2012 there was a step change in it’s capabilities.

Prior to 2012 hand coded AI image recognition algorithms were able to achieve about a 74%  image recognition level but in 2012, a deep learning algorithm achieved almost 85%, in one year.

And since then it’s been on a linear trend of improvements such that in 2015, current deep learning algorithms are better than human image recognition. Similar step function improvements were seen in speech recognition as well around 2012.

What drove the improvement?

Machine and deep learning depend on convolutional neural networks. These are layers of connected nodes. There are typically an input layer and output layer and N number of internal layers in a network. The connection weights between nodes control the response of the network.

Todays image recognition convolutional networks can have ~10 layers, billions of parameters, take ~30 Exaflops to train, using 10M images and took days to weeks to train.

Image recognition covolutional neural networks end up modeling the human visual cortex which has neurons to recognize edges and other specialized characteristics of a visual field.

The other thing that happened was that convolutional neural nets were translated to execute on GPUs in 2011. Neural networks had been around in AI since almost the very beginning but their computational complexity made them impossible to use effectively until recently. GPUs with 1000s of cores all able to double precision floating point operations made these networks now much more feasible.

Deep learning training of a network takes place through optimization of the node connections weights. This is done via a back propagation algorithm that was invented in the 1980’s.  Back propagation typically depends on “supervised learning” which adjust the weights of the connections between nodes to come closer to the correct answer, like recognizing Sarah in an image.

Deep learning today

Steinbach showed multiple examples of deep learning algorithms such as:

  • Mortgage prepayment predictor system which takes information about a mortgagee, location, and other data and predicts whether they will pre-pay their mortgage.
  • Car automation image recognition system which recognizes people, cars, lanes, road surfaces, obstacles and just about anything else in front of a car traveling a road.
  • X-ray diagnostic system that can diagnose diseases present in people from the X-ray images.

As far as I know all these algorithms use supervised learning and back propagation to train a convolutional network.

Steinbach did show an example of “un-supervised learning” which essentially was fed a bunch of images and did clustering analysis on them.  Not sure what the back propagation tried to optimize but the system was used to cluster the images in the set. It was able to identify one cluster of just military aircraft images out of the data.

The other advantage of convolutional neural networks is that they can be reused. E.g. the X-ray diagnostic system above used an image recognition neural net as a starting point and then ran it against a supervised set of X-rays with doctor provided diagnoses.

Another advantage of deep learning is that it can handle any number of dimensions. Mathematical optimization algorithms can handle a relatively few dimensions but deep learning can handle any number of dimensions.  The number of input dimensions, the number of nodes in each layer and number of layers in your network are only limited by computational power.

NVIDIA’s DGX a deep learning super computer

At the end of Stienbach’s talk he mentioned the DGX appliance designed by NVIDIA for AI research.

The appliance has 8 state of the art NVIDIA GPUs, connected over a high speed NVLink with anywhere from ~29K to ~41K cores depending on GPU selected, and is capable of 170 to 960 Flops (FP16).

Steinbach said this single 3u appliance would have been rated the number one supercomputer in 2004 beating out a building full of servers. If you were to connect 13 (I think) DGX’s together, you would qualify to be on the top 500 super computers in the world.



Photo credit(s): Steinbach’s “Deep Learning: Extracting Maximum Knowledge from Big Data using Big Compute” presentation at FMS 2017.