27,875 research outputs found
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks
Artificial neural networks (ANNs) trained using backpropagation are powerful
learning architectures that have achieved state-of-the-art performance in
various benchmarks. Significant effort has been devoted to developing custom
silicon devices to accelerate inference in ANNs. Accelerating the training
phase, however, has attracted relatively little attention. In this paper, we
describe a hardware-efficient on-line learning technique for feedforward
multi-layer ANNs that is based on pipelined backpropagation. Learning is
performed in parallel with inference in the forward pass, removing the need for
an explicit backward pass and requiring no extra weight lookup. By using binary
state variables in the feedforward network and ternary errors in
truncated-error backpropagation, the need for any multiplications in the
forward and backward passes is removed, and memory requirements for the
pipelining are drastically reduced. Further reduction in addition operations
owing to the sparsity in the forward neural and backpropagating error signal
paths contributes to highly efficient hardware implementation. For
proof-of-concept validation, we demonstrate on-line learning of MNIST
handwritten digit classification on a Spartan 6 FPGA interfacing with an
external 1Gb DDR2 DRAM, that shows small degradation in test error performance
compared to an equivalently sized binary ANN trained off-line using standard
back-propagation and exact errors. Our results highlight an attractive synergy
between pipelined backpropagation and binary-state networks in substantially
reducing computation and memory requirements, making pipelined on-line learning
practical in deep networks.Comment: Now also consider 0/1 binary activations. Memory access statistics
reporte
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
Small-variance asymptotics for Bayesian neural networks
Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data. In this thesis, we explore the use of small-variance asymptotics-an approach to yielding fast algorithms from probabilistic models-on various Bayesian neural network models. We first demonstrate how small-variance asymptotics shows precise connections between standard neural networks and BNNs; for example, particular sampling algorithms for BNNs reduce to standard backpropagation in the small-variance limit. We then explore a more complex BNN where the number of hidden units is additionally treated as a random variable in the model. While standard sampling schemes would be too slow to be practical, our asymptotic approach yields a simple method for extending standard backpropagation to the case where the number of hidden units is not fixed. We show on several data sets that the resulting algorithm has benefits over backpropagation on networks with a fixed architecture.2019-01-02T00:00:00
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