Micron Technology Incorporated

Paul Glendenning

Harold B Noyes

David R Brown

Junjuan Xu

Irene Junjuan Xu

Paul Dlugosch

Michael C Leventhal

Debra Bell

Eric Michael Jonas

Jeffery M Tanner

Yao Fu

Tommy Tracy, Ii

The Automata Processor Workbench (AP Workbench) is an application for creating and editing designs of AP networks (e.g., one or more portions of the state machine engine, one or more portions of the FSM lattice, or the like) based on, for example, an Automata Network Markup Language (ANML). For instance, the application may include a tangible, non-transitory computer-readable medium configured to store instructions executable by a processor of an electronic device, wherein the instructions include instructions to represent an automata network as a graph.

Methods and systems for creating automata networks

A device includes a plurality of blocks. Each block of the plurality of blocks includes a plurality of rows. Each row of the plurality of rows includes a plurality of configurable elements and a routing line, whereby each configurable element of the plurality of configurable elements includes a data analysis element comprising a plurality of memory cells, wherein the data analysis element is configured to analyze at least a portion of a data stream and to output a result of the analysis. Each configurable element of the plurality of configurable elements also includes a multiplexer configured to transmit the result to the routing line.

Devices for time division multiplexing of state machine engine signals

An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.

Space efficient random decision forest models implementation utilizing automata processors

A device, includes an instruction buffer. The instruction buffer is configured to store instructions related to at least a portion of a data stream to be analyzed by a state machine engine as the device. The state machine engine includes configurable elements configured to analyze the at least a portion of a data stream and to selectively output the result of the analysis. Additionally, the instruction buffer is configured to receive the indications as part of a direct memory access (DMA) transfer.

Systems and devices for accessing a state machine

A markup language is provided. The markup language describes the composition of automata networks. For example, the markup language uses elements that represent automata processing resources. These resources may include at least one of a state transition element, a counter element, and a Boolean element as respective automata processing resources.

Methods and systems for representing processing resources

Methods and systems for creating networks

Configuration content of electronic devices used for data analysis may be altered due to bit failure or corruption, for example. Accordingly, in one embodiment, a device includes a plurality of blocks, each block of the plurality of blocks includes a plurality of rows, each row of the plurality of rows includes a plurality of configurable elements, each configurable element of the plurality of configurable elements includes a data analysis element including a memory component programmed with configuration data. The data analysis element is configured to analyze at least a portion of a data stream based on the configuration data and to output a result of the analysis. The device also includes an error detection engine (EDE) configured to perform integrity validation of the configuration data.

Validation of a symbol response memory

In one embodiment, a system includes a bus interface including a first processor, an indirect address storage storing a number of indirect addresses, and a direct address storage storing a number of direct addresses. The system also includes a number of devices connected to the bus interface and configured to analyze data. Each device of the number of devices includes a state machine engine. The bus interface is configured to receive a command from a second processor and to transmit an address for loading into the state machine engine of at least one device of the number of devices. The address includes a first address from the number of indirect addresses or a second address from the number of direct addresses.

System and method for individual addressing

An apparatus includes a processing resource configured to receive a feature vector of a data stream. The feature vector includes a set of feature values. The processing resource is further configured to calculate a set of feature labels based at least in part on the set of feature values to generate a label vector, provide the label vector to another processing resource, and to receive a plurality of classifications corresponding to each feature label of the label vector from the other processing resource. The plurality of classifications are generated based at least in part on a respective range of feature values of the set of feature values. The processing resource is configured to then combine the plurality of classifications to generate a final classification of the data stream.

Growing community of inventors

Woodside, CA, United States of America

Paul Glendenning