79,162 research outputs found
On the relative proof complexity of deep inference via atomic flows
We consider the proof complexity of the minimal complete fragment, KS, of
standard deep inference systems for propositional logic. To examine the size of
proofs we employ atomic flows, diagrams that trace structural changes through a
proof but ignore logical information. As results we obtain a polynomial
simulation of versions of Resolution, along with some extensions. We also show
that these systems, as well as bounded-depth Frege systems, cannot polynomially
simulate KS, by giving polynomial-size proofs of certain variants of the
propositional pigeonhole principle in KS.Comment: 27 pages, 2 figures, full version of conference pape
On the proof complexity of deep inference
International audienceWe obtain two results about the proof complexity of deep inference: (1) Deep-inference proof systems are as powerful as Frege ones, even when both are extended with the Tseitin extension rule or with the substitution rule; (2) there are analytic deep-inference proof systems that exhibit an exponential speedup over analytic Gentzen proof systems that they polynomially simulate
Accelerating Training of Deep Neural Networks via Sparse Edge Processing
We propose a reconfigurable hardware architecture for deep neural networks
(DNNs) capable of online training and inference, which uses algorithmically
pre-determined, structured sparsity to significantly lower memory and
computational requirements. This novel architecture introduces the notion of
edge-processing to provide flexibility and combines junction pipelining and
operational parallelization to speed up training. The overall effect is to
reduce network complexity by factors up to 30x and training time by up to 35x
relative to GPUs, while maintaining high fidelity of inference results. This
has the potential to enable extensive parameter searches and development of the
largely unexplored theoretical foundation of DNNs. The architecture
automatically adapts itself to different network sizes given available hardware
resources. As proof of concept, we show results obtained for different bit
widths.Comment: Presented at the 26th International Conference on Artificial Neural
Networks (ICANN) 2017 in Alghero, Ital
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