110 research outputs found
Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with binary weights
Significant success has been reported recently using deep neural networks for
classification. Such large networks can be computationally intensive, even
after training is over. Implementing these trained networks in hardware chips
with a limited precision of synaptic weights may improve their speed and energy
efficiency by several orders of magnitude, thus enabling their integration into
small and low-power electronic devices. With this motivation, we develop a
computationally efficient learning algorithm for multilayer neural networks
with binary weights, assuming all the hidden neurons have a fan-out of one.
This algorithm, derived within a Bayesian probabilistic online setting, is
shown to work well for both synthetic and real-world problems, performing
comparably to algorithms with real-valued weights, while retaining
computational tractability
Neural Network Adaptations to Hardware Implementations
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and nonideal responses, and restraining computational complexity. Furthermore, a broad range of hardware-friendly learning rules is presented, which allow for simpler and more reliable hardware implementations. The relevance of these neural network adaptations to hardware is illustrated by their application in existing hardware implementations
Out of equilibrium Statistical Physics of learning
In the study of hard optimization problems, it is often unfeasible to achieve
a full analytic control on the dynamics of the algorithmic processes that
find solutions efficiently. In many cases, a static approach is able to provide
considerable insight into the dynamical properties of these algorithms: in fact,
the geometrical structures found in the energetic landscape can strongly affect
the stationary states and the optimal configurations reached by the solvers.
In this context, a classical Statistical Mechanics approach, relying on the
assumption of the asymptotic realization of a Boltzmann Gibbs equilibrium,
can yield misleading predictions when the studied algorithms comprise some
stochastic components that effectively drive these processes out of equilibrium.
Thus, it becomes necessary to develop some intuition on the relevant features
of the studied phenomena and to build an ad hoc Large Deviation analysis,
providing a more targeted and richer description of the geometrical properties
of the landscape. The present thesis focuses on the study of learning processes
in Artificial Neural Networks, with the aim of introducing an out of equilibrium
statistical physics framework, based on the introduction of a local entropy
potential, for supporting and inspiring algorithmic improvements in the field
of Deep Learning, and for developing models of neural computation that can
carry both biological and engineering interest
Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments
Visual place recognition (VPR) is a robot’s ability to determine whether a place was visited before using visual data. While conventional handcrafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this article, we take a multistep approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the VPR problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed by seven times
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