A new approach is proposed to support efficient convolution for deep learning by vectorizing
multi-dimensional input data for multi-dimensional fast Fourier transform (FFT) and direct …

A hardware-based programmable deep learning processor (DLP) is proposed, wherein the
DLP comprises with a plurality of accelerators dedicated for deep learning processing …

Apparatuses, systems, and techniques to transform input data for training neural networks. In
at least one embodiment, one or more data transforms are identified in a sequence of data …

Methods and systems include a neural network system that includes a neural network
accelerator. The neural network accelerator includes multiple processing engines coupled …

Hardware implementations of DNNs and related methods with a variable output data format.
Specifically, in the hardware implementations and methods described herein the hardware …

Methods and systems for training a neural network include sampling multiple local sub-
networks from a global neural network. The local sub-networks include a subset of neurons …

A deep learning computer apparatus and corresponding methods render multiple disparate
data type items, along with corresponding feature data, into a single encapsulated file format …

A computer processor may include a number of cores, a shared cache shared among the
cores, and a local cache associated with each core and used by that core only. Input data for …

Abstract Systems and methods for training neural networks. One embodiment is a system
that includes a memory configured to store samples of training data for a Deep Neural …

Techniques in advanced deep learning provide improvements in one or more of accuracy,
performance, energy efficiency, and cost. In a first embodiment, a scaled array of processing …