BACKGROUND

The present invention relates generally to streaming algorithms, and more specifically, to matrix sketching using analog crossbar architectures.

Deep neural networks (DNN) have made tremendous advances, in some cases surpassing human level performance, tackling challenging problems such as speech recognition, natural language processing, image classification, and machine translation. Training of large DNNs, however, is a time consuming and computationally intensive task that demands datacenter scale computational resources composed of state of the art graphical processing units (GPUs). There have been attempts to accelerate deep learning workloads beyond GPUs by designing custom hardware utilizing reduced precision arithmetic to improve throughput and energy efficiency of the underlying complementary metal oxide semiconductor (CMOS) technology. Alternative to digital approaches, resistive cross-point device arrays have been proposed to further increase the throughput and energy efficiency of the overall system by performing vector-matrix multiplications in the analog domain.

SUMMARY

In accordance with an embodiment, a method is provided for performing matrix sketching by employing an analog crossbar architecture. The method includes updating a first matrix for a first period of time, copying the first matrix into a dynamic correction computing device, switching to a second matrix to update the second matrix for a second period of time, as the second matrix is low rank updated, feeding the first matrix with first stochastic pulses to reset the first matrix back to a first matrix symmetry point, copying the second matrix into the dynamic correction computing device, switching back to the first matrix to update the first matrix for a third period of time, and as the first matrix is low rank updated, feeding the second matrix with second stochastic pulses to reset the second matrix back to a second matrix symmetry point. The updates are low-rank updates.

In accordance with another embodiment, a system is provided for performing matrix sketching by employing an analog crossbar architecture. The system includes a memory and one or more processors in communication with the memory configured to update a first matrix for a first period of time, copy the first matrix into a dynamic correction computing device, switch to a second matrix to update the second matrix for a second period of time, as the second matrix is low rank updated, feed the first matrix with first stochastic pulses to reset the first matrix back to a first matrix symmetry point, copy the second matrix into the dynamic correction computing device, switch back to the first matrix to update the first matrix for a third period of time, and as the first matrix is low rank updated, feed the second matrix with second stochastic pulses to reset the second matrix back to a second matrix symmetry point. The updates are low-rank updates.

In accordance with yet another embodiment, a non-transitory computer-readable storage medium comprising a computer-readable program for performing matrix sketching by employing an analog crossbar architecture is presented. The non-transitory computer-readable storage medium performs the steps of updating a first matrix for a first period of time, copying the first matrix into a dynamic correction computing device, switching to a second matrix to update the second matrix for a second period of time, as the second matrix is low rank updated, feeding the first matrix with first stochastic pulses to reset the first matrix back to a first matrix symmetry point, copying the second matrix into the dynamic correction computing device, switching back to the first matrix to update the first matrix for a third period of time, and as the first matrix is low rank updated, feeding the second matrix with second stochastic pulses to reset the second matrix back to a second matrix symmetry point. The updates are low-rank updates.

In accordance with an embodiment, a method is provided for performing matrix sketching by employing an analog crossbar architecture. The method includes applying dimensionality reduction to streaming data using outer product updates, and once the dimensionality reduction is applied to an entire input, moving a sketched matrix to a digital computer to perform regression analysis. The updates are low-rank updates.

In accordance with another embodiment, a system is provided for performing matrix sketching by employing an analog crossbar architecture. The system includes a memory and one or more processors in communication with the memory configured to apply dimensionality reduction to streaming data using outer product updates, and once the dimensionality reduction is applied to an entire input, move a sketched matrix to a digital computer to perform regression analysis. The updates are low-rank updates.

It should be noted that the exemplary embodiments are described with reference to different subject-matters. In particular, some embodiments are described with reference to method type claims whereas other embodiments have been described with reference to apparatus type claims. However, a person skilled in the art will gather from the above and the following description that, unless otherwise notified, in addition to any combination of features belonging to one type of subject-matter, also any combination between features relating to different subject-matters, in particular, between features of the method type claims, and features of the apparatus type claims, is considered as to be described within this document.

These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.

