Abstract: A system for evaluating the convergence to a solution for a matrix equation comprises at least one reconfigurable computing device such as a field programmable gate array (FPGA), an update storage element, a conversion element, a summation unit, and a comparator. The FPGA includes a plurality of configurable logic elements and a plurality of configurable storage elements, which are utilized to form the update storage element, the conversion element, the summation unit, and the comparator. The update storage element is configured to store a plurality of updates. The conversion element determines the absolute value of the updates. The summation unit accumulates the absolute values of the updates to produce a total sum, which is compared to a convergence factor by the comparator. Convergence is signaled when the total sum is less than the convergence factor.

Abstract: A system and method for storing and retrieving a sparse matrix from memory of a computing device while minimizing the amount of data stored and costly jumps in memory. The computing device may be an FPGA having memory and processing elements. The method comprises storing non-zero data elements of the matrix in a data array and storing their corresponding column address values in a column index array. To read this stored data from memory, each preceding value of the column index array may be compared with each current value of the column index array to determine if the data array value corresponding with the current column index array value belongs on the next row of the matrix. The method may include pre-ordering the matrix with zero-pad placeholders or creating a row increment pointer array which typically stores fewer values than the number of rows in the matrix.

Abstract: A system for determining the median of a plurality of data values comprises a plurality of field programmable gate arrays (FPGA), a plurality of inter FPGA links, an input router, a plurality of median modules, and a plurality of output transfer modules. Each FPGA includes a plurality of configurable logic elements and configurable storage elements from which the other components are formed. The inter FPGA link allows communication from one FPGA to another. The input router receives the plurality of data values and creates a plurality of data streams. The median module receives at least one data stream, increments a plurality of counters corresponding to a single data value within the range of data values, and determines the median by accumulating the contents of each counter. The output transfer module transfers the median to an external destination along with performance statistics of the determination of the median.

Abstract: A system and method for storing and retrieving a sparse matrix from memory of a computing device while minimizing the amount of data stored and costly jumps in memory. The computing device may be an FPGA having memory and processing elements. The method comprises storing non-zero data elements of the matrix in a data array and storing their corresponding column address values in a column index array. To read this stored data from memory, each preceding value of the column index array may be compared with each current value of the column index array to determine if the data array value corresponding with the current column index array value belongs on the next row of the matrix. The method may include pre-ordering the matrix with zero-pad placeholders or creating a row increment pointer array which typically stores fewer values than the number of rows in the matrix.

Abstract: A system and method for storing and retrieving a sparse matrix from memory of a computing device while minimizing the amount of data stored and costly jumps in memory. The computing device may be an FPGA having memory and processing elements. The method comprises storing non-zero data elements of the matrix in a data array and storing their corresponding column address values in a column index array. To read this stored data from memory, each preceding value of the column index array may be compared with each current value of the column index array to determine if the data array value corresponding with the current column index array value belongs on the next row of the matrix. The method may include pre-ordering the matrix with zero-pad placeholders or creating a row increment pointer array which typically stores fewer values than the number of rows in the matrix.

Abstract: A computing system (10) includes a plurality of hardware computing resources (12-36) controlled at least in part by a plurality of autonomous computing agents (40,42,44). Each autonomous computing agent (40,42,44) includes or has access to operating information including processing information (46), resource information (48), optimization information (50), and communication information (52). The computing agents (40,42,44) collaborate to optimize performance of the system (10) and to assign computing tasks to the resources (12-36) according to a predetermined strategy. The predetermined strategy may seek to optimize speed, power, or communication efficiency of the system 10. Each agent (40,42,44) may optimize performance of the system (10) by assigning tasks to best-fit resources or by reconfiguring one or more resources.

Abstract: A method for using a system to compute a solution to a partial differential equation (PDE) broadly comprises the steps of determining the true accuracy required (TAR) to solve the PDE, determining an architecture according to the TAR that performs a plurality of calculations to solve the PDE, determining a time allowed (TA) and a time required (TR) based on the architecture to solve the PDE, rejecting the PDE if the TR is less than or equal to the TA, configuring a plurality of programmable devices with the architecture, initiating the calculations, and ceasing the calculations when an accuracy criteria is met or when the TA expires. The system broadly comprises a plurality of programmable devices, a plurality of storage elements, a device bus, a plurality of printed circuit (PC) boards, and a board to board bus.

Abstract: A system for a conjugate gradient iterative linear solver that calculates the solution to a matrix equation comprises a plurality of gamma processing elements, a plurality of direction vector processing elements, a plurality of x-vector processing elements, an alpha processing element, and a beta processing element. The gamma processing elements may receive an A-matrix and a direction vector, and may calculate a q-vector and a gamma scalar. The direction vector processing elements may receive a beta scalar and a residual vector, and may calculate the direction vector. The x-vector processing elements may receive an alpha scalar, the direction vector, and the q-vector, and may calculate an x-vector and the residual vector. The alpha processing element may receive the gamma scalar and a delta scalar, and may calculate the alpha scalar. The beta processing element may receive the residual vector, and may calculate the delta scalar and the beta scalar.

Abstract: Problem solution speed may be increased by dynamically changing processing device computational hardware configuration in concert with respective mathematical phases of an algorithm to match accuracy demands at various phases of computation. Smaller but faster hardware structures may be increased in size using real-time partial or full reconfiguration of a processing device to apply the smallest and fastest possible computational structure for the needed accuracy during each of multiple computational phases.

Abstract: A computing architecture comprises a plurality of processing elements to perform data processing calculations, a plurality of memory elements to store the data processing results, and a reconfigurable interconnect network to couple the processing elements to the memory elements. The reconfigurable interconnect network includes a switching element, a control element, a plurality of processor interface units, a plurality of memory interface units, and a plurality of application control units. In various embodiments, the processing elements and the interconnect network may be implemented in a field-programmable gate array.

Abstract: Application Specific Integrated Circuit (“ASIC”) devices, such as Field Programmable Gate Arrays (“FPGAs”), may be interconnected using serial I/O connections, such as high speed multi-gigabit serial transceiver (“MGT”) connections. For example, serial I/O connections may be employed to interconnect a pair of ASICs to create a high bandwidth, low signal count connection, and in a manner so that any given pair of multiple ASIC devices on a single circuit card may communicate with each other through no more than one serial data communication link connection step. A reconfigurable hardware architecture (“RHA”) may be configured to include a communications infrastructure that uses a high-bandwidth packet router to establish standard communications protocols between multiple interfaces and/or multiple devices that may be present on a single circuit card. Dynamically-sized data packets, sized in accordance with the amount of data ready to be sent, are transferred between the devices and/or interfaces on the card.

Abstract: A system for solving linear equations comprises a first circuit including a first multiplication module for multiplying a first row of a matrix by a first instance of a vector variable to generate a first product, and a first linear solver module for calculating an updated first element of the vector variable using the first product. A second circuit includes a second multiplication module for multiplying a second row of the matrix by a second instance of the vector variable to generate a second product, and a second linear solver module for calculating an updated second element of the vector variable using the second product. An interface module updates the second instance of the vector variable with the first updated element, and updates the first instance of the vector variable with the second updated element.

Abstract: A system for correcting image characteristic data from a plurality of pixels comprises at least one field programmable gate array (FPGA), a lookup table, and a correction module. The FPGA may include a plurality of configurable logic elements and a plurality of configurable storage elements. The lookup table may be accessible by the FPGA and may store a plurality of correction components associated with each pixel, including a gain value, an offset value, and a bad pixel value. The correction module may be formed from the configurable logic elements and configurable storage elements and may receive the characteristic data and the correction components. The correction module may generate corrected data for each characteristic data by utilizing the gain value, the offset value, and the bad pixel value.

Abstract: A method for using a system to compute a solution to a partial differential equation (PDE) broadly comprises the steps of determining the true accuracy required (TAR) to solve the PDE, determining an architecture according to the TAR that performs a plurality of calculations to solve the PDE, determining a time allowed (TA) and a time required (TR) based on the architecture to solve the PDE, rejecting the PDE if the TR is less than or equal to the TA, configuring a plurality of programmable devices with the architecture, initiating the calculations, and ceasing the calculations when an accuracy criteria is met or when the TA expires. The system broadly comprises a plurality of programmable devices, a plurality of storage elements, a device bus, a plurality of printed circuit (PC) boards, and a board to board bus.

Abstract: Problem solution speed may be increased by dynamically changing processing device computational hardware configuration in concert with respective mathematical phases of an algorithm to match accuracy demands at various phases of computation. Smaller but faster hardware structures may be increased in size using real-time partial or full reconfiguration of a processing device to apply the smallest and fastest possible computational structure for the needed accuracy during each of multiple computational phases.

Abstract: A scalable and/or reconfigurable true-time-delay analog beamformer system having a hierarchical distributed control architecture composed of an arbitrary number of reconfigurable and scalable units. The beamformer system may be applied to an antenna array with an arbitrary number of elements in a scalable manner and the configuration of the beamformer system may be implemented so that it is capable of reconfiguration by changing its beam-position mapping, either dynamically or at install-time. The number of beams or beam positions that are desired advantageously do not need to be known prior to the design or selection of the beamformer system.

Abstract: A system for a conjugate gradient iterative linear solver that calculates the solution to a matrix equation comprises a plurality of gamma processing elements, a plurality of direction vector processing elements, a plurality of x-vector processing elements, an alpha processing element, and a beta processing element. The gamma processing elements may receive an A-matrix and a direction vector, and may calculate a q-vector and a gamma scalar. The direction vector processing elements may receive a beta scalar and a residual vector, and may calculate the direction vector. The x-vector processing elements may receive an alpha scalar, the direction vector, and the q-vector, and may calculate an x-vector and the residual vector. The alpha processing element may receive the gamma scalar and a delta scalar, and may calculate the alpha scalar. The beta processing element may receive the residual vector, and may calculate the delta scalar and the beta scalar.

Abstract: A system for evaluating the convergence to a solution for a matrix equation comprises at least one reconfigurable computing device such as a field programmable gate array (FPGA), an update storage element, a conversion element, a summation unit, and a comparator. The FPGA includes a plurality of configurable logic elements and a plurality of configurable storage elements, which are utilized to form the update storage element, the conversion element, the summation unit, and the comparator. The update storage element is configured to store a plurality of updates. The conversion element determines the absolute value of the updates. The summation unit accumulates the absolute values of the updates to produce a total sum, which is compared to a convergence factor by the comparator. Convergence is signaled when the total sum is less than the convergence factor.

Abstract: A system for identifying a person includes at least one biometric sensor for sensing a biometric characteristic of the person; at least one signal sensor for sensing a signal emitted from a device carried by the person; and a computing device for comparing the sensed biometric characteristic and the sensed signal to known characteristics of the person in an attempt to identify the person.

Abstract: A system for solving large-scale matrix equations comprises a plurality of field programmable gate arrays (FPGAs), a plurality of memory elements, a plurality of memory element controllers, and a plurality of processing elements. The FPGAs may include a plurality of configurable logic elements and a plurality of configurable storage elements. The memory elements may be accessible by the FPGAs and may store a matrix and a first vector. The memory element controllers may be formed from configurable logic elements and configurable storage elements and may supply at least a portion of a row of the matrix and at least a portion of the first vector. Each processing element may receive at least the row of the matrix and the first vector and solve an iteration for one element of the first vector.