152,198 research outputs found
Direct Feedback Alignment with Sparse Connections for Local Learning
Recent advances in deep neural networks (DNNs) owe their success to training
algorithms that use backpropagation and gradient-descent. Backpropagation,
while highly effective on von Neumann architectures, becomes inefficient when
scaling to large networks. Commonly referred to as the weight transport
problem, each neuron's dependence on the weights and errors located deeper in
the network require exhaustive data movement which presents a key problem in
enhancing the performance and energy-efficiency of machine-learning hardware.
In this work, we propose a bio-plausible alternative to backpropagation drawing
from advances in feedback alignment algorithms in which the error computation
at a single synapse reduces to the product of three scalar values. Using a
sparse feedback matrix, we show that a neuron needs only a fraction of the
information previously used by the feedback alignment algorithms. Consequently,
memory and compute can be partitioned and distributed whichever way produces
the most efficient forward pass so long as a single error can be delivered to
each neuron. Our results show orders of magnitude improvement in data movement
and improvement in multiply-and-accumulate operations over
backpropagation. Like previous work, we observe that any variant of feedback
alignment suffers significant losses in classification accuracy on deep
convolutional neural networks. By transferring trained convolutional layers and
training the fully connected layers using direct feedback alignment, we
demonstrate that direct feedback alignment can obtain results competitive with
backpropagation. Furthermore, we observe that using an extremely sparse
feedback matrix, rather than a dense one, results in a small accuracy drop
while yielding hardware advantages. All the code and results are available
under https://github.com/bcrafton/ssdfa.Comment: 15 pages, 8 figure
Exascale Deep Learning for Climate Analytics
We extract pixel-level masks of extreme weather patterns using variants of
Tiramisu and DeepLabv3+ neural networks. We describe improvements to the
software frameworks, input pipeline, and the network training algorithms
necessary to efficiently scale deep learning on the Piz Daint and Summit
systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained
throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up
to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel
efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor
Cores, a half-precision version of the DeepLabv3+ network achieves a peak and
sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November
11-16, 2018, Dallas, TX, US
Measuring the Impact of Spectre and Meltdown
The Spectre and Meltdown flaws in modern microprocessors represent a new
class of attacks that have been difficult to mitigate. The mitigations that
have been proposed have known performance impacts. The reported magnitude of
these impacts varies depending on the industry sector and expected workload
characteristics. In this paper, we measure the performance impact on several
workloads relevant to HPC systems. We show that the impact can be significant
on both synthetic and realistic workloads. We also show that the performance
penalties are difficult to avoid even in dedicated systems where security is a
lesser concern
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual
exclusion of labels, hierarchical multi-label classification (HMLC) aims to
assign multiple labels to every instance with the labels organized under
hierarchical relations. Besides the labels, since linguistic ontologies are
intrinsic hierarchies, the conceptual relations between words can also form
hierarchical structures. Thus it can be a challenge to learn mappings from word
hierarchies to label hierarchies. We propose to model the word and label
hierarchies by embedding them jointly in the hyperbolic space. The main reason
is that the tree-likeness of the hyperbolic space matches the complexity of
symbolic data with hierarchical structures. A new Hyperbolic Interaction Model
(HyperIM) is designed to learn the label-aware document representations and
make predictions for HMLC. Extensive experiments are conducted on three
benchmark datasets. The results have demonstrated that the new model can
realistically capture the complex data structures and further improve the
performance for HMLC comparing with the state-of-the-art methods. To facilitate
future research, our code is publicly available
Learning from distributed data sources using random vector functional-link networks
One of the main characteristics in many real-world big data scenarios is their distributed nature. In a machine learning context, distributed data, together with the requirements of preserving privacy and scaling up to large networks, brings the challenge of designing fully decentralized training protocols. In this paper, we explore the problem of distributed learning when the features of every pattern are available throughout multiple agents (as is happening, for example, in a distributed database scenario). We propose an algorithm for a particular class of neural networks, known as Random Vector Functional-Link (RVFL), which is based on the Alternating Direction Method of Multipliers optimization algorithm. The proposed algorithm allows to learn an RVFL network from multiple distributed data sources, while restricting communication to the unique operation of computing a distributed average. Our experimental simulations show that the algorithm is able to achieve a generalization accuracy comparable to a fully centralized solution, while at the same time being extremely efficient
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