Results 1 to 10 of about 154,546 (314)
Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks
IEEE Access, 2021 Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy. Sparse CNNs, however, present their own set of challenges including non-
Mahmood Azhar Qureshi, Arslan Munir
doaj +1 more source
MMSE Estimation of Sparse Lévy Processes [PDF]
IEEE Transactions on Signal Processing, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ulugbek Kamilov +3 more
openaire +1 more source
Sparse multiscale gaussian process regression [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with ...
Christian Walder +2 more
openaire +3 more sources
Current Developments of Sparse Microwave Imaging
Leida xuebao, 2014 The sparse microwave imaging combines the sparse signal processing theory with radar imaging to obtain new theory, new system, and new methodology of microwave imaging.
Wu Yi-rong +5 more
doaj +1 more source
Frame coherence and sparse signal processing [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 The sparse signal processing literature often uses random sensing matrices to obtain performance guarantees. Unfortunately, in the real world, sensing matrices do not always come from random processes. It is therefore desirable to evaluate whether an arbitrary matrix, or frame, is suitable for sensing sparse signals.
Dustin G. Mixon +2 more
openaire +2 more sources
Gaussian and sparse processes are limits of generalized Poisson processes [PDF]
Applied and Computational Harmonic Analysis, 2020 The theory of sparse stochastic processes offers a broad class of statistical models to study signals. In this framework, signals are represented as realizations of random processes that are solution of linear stochastic differential equations driven by white Lévy noises.
Fageot, Julien +2 more
openaire +2 more sources
Sparse Gaussian Processes on Discrete Domains [PDF]
IEEE Access, 2021 IEEE Access ...
Vincent Fortuin +3 more
openaire +3 more sources
Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding
Micromachines, 2023 To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention.
Yueran Qi +9 more
doaj +1 more source
RSNN: A Software/Hardware Co-Optimized Framework for Sparse Convolutional Neural Networks on FPGAs
IEEE Access, 2021 Convolutional Neural Networks (CNNs) have been shown to be very useful in image recognition and other Artificial Intelligence (AI) applications, however, at the expense of intensive computation requirement.
Weijie You, Chang Wu
doaj +1 more source
Asynchronous processing of sparse signals
IET Signal Processing, 2014 Unlike synchronous processing, asynchronous processing is more efficient in biomedical and sensing networks applications as it is free from aliasing constraints and quantization error in the amplitude, it allows continuous–time processing and more importantly data is only acquired in significant parts of the signal. We consider signal decomposers based
Azime Can-Cimino +2 more
openaire +1 more source