OFA DNNs, cutting the carbon out of AI

Read an article (Reducing the carbon footprint of AI… in Science Daily) the other day about a new approach to reducing the energy demands for AI deep neural net (DNN) training and inferencing. The article was reporting on a similar piece in MIT News but both were discussing a technique original outlined in a ICLR 2020 (Int. Conf. on Learning Representations) paper, Once-for-all: Train one network & specialize it for efficient deployment.

The problem stems from the amount of energy it takes to train a DNN and use it for inferencing. In most cases, training and (more importantly) inferencing can take place on many different computational environments, from IOT devices, to cars, to HPC super clusters and everything in between. In order to create DNN inferencing algorithms for use in all these environments, one would have to train a different DNN for each. Moreover, if you’re doing image recognition applications, resolution levels matter. Resolution levels would represent a whole set of more required DNNs that would need to be trained.

The authors of the paper suggest there’s a better approach. Train one large OFA (once-for-all) DNN, that covers the finest resolution and largest neural net required in such a way that smaller, sub-nets could be extracted and deployed for less weighty computational and lower resolution deployments.

The authors contend the OFA approach takes less overall computation (and energy) to create and deploy than training multiple times for each possible resolution and deployment environment. It does take more energy to train than training a few (4-7 judging by the chart) DNNs, but that can be amortized over a vastly larger set of deployments.

OFA DNN explained

Essentially the approach is to train one large (OFA) DNN, with sub-nets that can be used by themselves. The OFA DNN sub-nets have been optimized for different deployment dimensions such as DNN model width, depth and kernel size as well as resolution levels.

While DNN width is purely the number of numeric weights in each layer, and DNN depth is the number of layers, Kernel size is not as well known. Kernels were introduced in convolutional neural networks (CovNets) to identify the number of features that are to be recognized. For example, in human faces these could be mouths, noses, eyes, etc. All these dimensions + resolution levels are used to identify all possible deployment options for an OFA DNN.

OFA secrets

One key to the OFA success is that any model (sub-network) selected actually shares the weights of all of its larger brethren. That way all the (sub-network) models can be represented by the same DNN and just selecting the dimensions of interest for your application. If you were to create each and every DNN, the number would be on the order of 10**19 DNNs for the example cited in the paper with depth using {2,3,4) layers, width using {3,4,6} and kernel sizes over 25 different resolution levels.

In order to do something like OFA, one would need to train for different objectives (once for each different resolution, depth, width and kernel size). But rather than doing that, OFA uses an approach which attempts to shrink all dimensions at the same time and then fine tunes that subsets NN weights for accuracy. They call this approach progressive shrinking.

Progressive shrinking, training for different dimensions

Essentially they train first with the largest value for each dimension (the complete DNN) and then in subsequent training epochs reduce one or more dimensions required for the various deployments and just train that subset. But these subsequent training passes always use the pre-trained larger DNN weights. As they gradually pick off and train for every possible deployment dimension, the process modifies just those weights in that configuration. This way the weights of the largest DNN are optimized for all the smaller dimensions required. And as a result, one can extract a (defined) subnet with the dimensions needed for your inferencing deployments.

They use a couple of tricks when training the subsets. For example, when training for smaller kernel sizes, they use the center most kernels and transform their weights using a transformation matrix to improve accuracy with less kernels. When training for smaller depths, they use the first layers in the DNN and ignore any layers lower in the model. Training for smaller widths, they sort each layer for the highest weights, thus ensuring they retain those parameters that provide the most sensitivity.

It’s sort of like multiple video encodings in a single file. Rather than having a separate file for every video encoding format (Mpeg 2, Mpeg 4, HVEC, etc.), you have one file, with all encoding formats embedded within it. If for example you needed Mpeg-4, one could just extract those elements of the video file representing that encoding level

OFA DNN results

In order to do OFA, one must identify, ahead of time, all the potential inferencing deployments (depth, width, kernel sizes) and resolution levels to support. But in the end, you have a one size fits all trained DNN whose sub-nets can be selected and deployed for any of the pre-specified deployments.

The authors have shown (see table and figure above) that OFA beats (in energy consumed and accuracy level) other State of the Art (SOTA) and Neural (network) Architectural Search (NAS) approaches to training multiple DNNs.

The report goes on to discuss how OFA could be optimized to support different latency (inferencing response time) requirements as well as diverse hardware architectures (CPU, GPU, FPGA, etc.).

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When I first heard of OFA DNN, I thought we were on the road to artificial general intelligence but this is much more specialized than that. It’s unclear to me how many AI DNNs have enough different deployment environments to warrant the use of OFA but with the proliferation of AI DNNs for IoT, automobiles, robots, etc. their will come a time soon where OFA DNNs and its competition will become much more important.

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