528 results about "Arithmetic logic unit" patented technology

A digital camera has a sensor for sensing an image, a processor for modifying the sensed image in accordance with instructions input into the camera and an output for outputting the modified image where the processor includes a series of processing elements arranged around a central crossbar switch. The processing elements include an Arithmetic Logic Unit (ALU) acting under the control of a writeable microcode store, an internal input and output FIFO for storing pixel data to be processed by the processing elements and the processor is interconnected to a read and write FIFO for reading and writing pixel data of images to the processor. Each of the processing elements can be arranged in a ring and each element is also separately connected to its nearest neighbors. The ALU receives a series of inputs interconnected via an internal crossbar switch to a series of core processing units within the ALU and includes a number of internal registers for the storage of temporary data. The core processing units can include at least one of a multiplier, an adder and a barrel shifter. The processing elements are further connected to a common data bus for the transfer of a pixel data to the processing elements and the data bus is interconnected to a data cache which acts as an intermediate cache between the processing elements and a memory store for storing the images.

A memory cell mat is divided into a plurality of entries, and an arithmetic logic unit is arranged corresponding to each entry. Between the entries and the corresponding arithmetic logic units, arithmetic/logic operation is executed in bit-serial and entry-parallel manner. Where parallel operation is not very effective, data is transferred in entry-serial and bit-parallel manner to a group of processors provided at a lower portion of the memory mat. In this manner, a large amount of data can be processed at high speed regardless of the contents of operation or data bit width.

An array of non-volatile memory cells arranged in logical columns and logical rows, and associated circuitry to enable reading or writing one or more memory cells on a row in parallel. In some embodiments, the array of memory cells may include a phase change material. In some embodiments, the circuitry may include a write driver, a read driver, a sense amplifier, and circuitry to isolate the memory cells from the sense amplifier with extended refresh. In some embodiments, the circuitry may further include shift registers and one or more arithmetic logic units to provide a video memory.

A processing system comprising processors and the dynamically configurable communication elements coupled together in an interspersed arrangement. The processors each comprise at least one arithmetic logic unit, an instruction processing unit, and a plurality of processor ports. The dynamically configurable communication elements each comprise a plurality of communication ports, a first memory, and a routing engine. For each of the processors, the plurality of processor ports is configured for coupling to a first subset of the plurality of dynamically configurable communication elements. For each of the dynamically configurable communication elements, the plurality of communication ports comprises a first subset of communication ports configured for coupling to a subset of the plurality of processors and a second subset of communication ports configured for coupling to a second subset of the plurality of dynamically configurable communication elements.

Provided in the invention is a time-division-multiplexing general neural network processor comprising at least one storage unit (100), at least one storage unit controller (101), at least one arithmetic logic unit (103), and a control unit (102). To be specific, the at least one storage unit (100) is used for storing an instruction and data. The at least one storage unit controller (101) corresponds to the at least one storage unit (100) and accesses the corresponding storage unit (100). The at least one arithmetic logic unit (103) is used for executing neural network computing. The control unit (102) connected with the at least one storage unit controller (101) and the at least one arithmetic logic unit (103) can obtain the instruction stored by the at least one storage unit (100) by the at least one storage unit controller (101) and parse the instruction to control the at least one arithmetic logic unit (103) to execute computation. The provided general neural network processor with high universality is suitable for computation of a large-scale neural network.

Briefly, graphics data processing logic includes a plurality of parallel arithmetic logic units (ALUs), such as floating point processors or any other suitable logic, that operate as a vector processor on at least one of pixel data and vertex data (or both) and a programmable storage element that contains data representing which of the plurality of arithmetic logic units are not to receive data for processing. The graphics data processing logic also includes parallel ALU data packing logic that is operatively coupled to the plurality of arithmetic logic processing units and to the programmable storage element to pack data only for the plurality of arithmetic logic units identified by the data in the programmable storage element as being enabled.

The present invention comprises a method for forecasting future economic conditions, land utilization and transportation network utilization and performance of a metropolitan area having a plurality of economic zones, the method includes the steps of: a) receiving a set of calibration values from a first input device; b) calculating a set of calibration constants with a first arithmetic logic unit; c) receiving a set of initial values for a set of desired outputs from a second input device, wherein the set of desired outputs includes regional economic, land use and transportation outputs; d) calculating the regional economic and land use outputs with a second arithmetic logic unit, wherein the regional economic and land use outputs include a first group of variable travel demands; e) calculating an origin to destination matrix with a third arithmetic logic unit, wherein the origin to destination matrix includes two-way daily person trips between an origin economic zone and a destination economic zone; f) calculating the transportation outputs with a fourth arithmetic logic unit, wherein the transportation outputs include a second group of variable travel demands; h) repeating steps d) through f) until the first group of variable travel demands is substantially the same as the second group of variable travel demands; and, g) providing the set of desired outputs to an output device.

A digital computer performs read-modify-write (RMW) processing on each bit of a row of memory in parallel, in one operation cycle, comprising: (a) addressing a memory, (b) reading each bit of a row of data from the memory in parallel, (c) performing the same computational operation on each bit of the data in parallel, using an arithmetic logic unit (ALU) in a dedicated processing element, and (d) writing the result of the operation back into the original memory location for each bit in the row.

A semiconductor device of the present invention includes: at least one of non-volatile memory unit operable to store data; at least one of an arithmetic-logic unit operable to perform an arithmetic-logic operation using data which is stored in the memory unit and data that is inputted from outside; and an output unit operable to output a result of arithmetic-logic operation performed by the arithmetic-logic unit; wherein the memory unit, the arithmetic-logic unit, and the output unit are included in a functional block, and an output line of each of the memory unit is connected only to one of at least one of the arithmetic-logic unit.

A programmable motion estimation module for processing pixel values from a sequence of digital video images. The module includes a programmable microcontroller, scalar and control register files, arithmetic logic units, a direct memory access unit and a vector array processor. The vector array processor includes a series of processing elements, a memory subsystem for storing pixel values and a crossbar switch for distributing pixel values from the memory subsystem to the processing elements. The module provides a flexible platform that can be programmed to implement a variety of different Motion Estimation (ME) algorithms using an associated Instruction Set Architecture without the need to modify the hardware.

A driver assistance system for an agricultural working machine, in particular a combine harvester has a large number of working mechanisms, an arithmetic logic unit, and at least one display unit, in which the arithmetic logic unit may process information generated by machine-internal sensor systems, external information, and information that is storable in the arithmetic logic unit, and in which the driver assistance system overcomes—with consideration for at least a selection of the available information—critical functions of the agricultural working machine in that efficiency parameters of the agricultural working machine are optimized via interactive, natural-language communication between the operator of the agricultural working machine and the driver assistance system.

Accelerator systems and methods are disclosed that utilize FPGA technology to achieve better parallelism and processing speed. A Field Programmable Gate Array (FPGA) is configured to have a hardware logic performing computations associated with a neural network training algorithm, especially a Web relevance ranking algorithm such as LambaRank. The training data is first processed and organized by a host computing device, and then streamed to the FPGA for direct access by the FPGA to perform high-bandwidth computation with increased training speed. Thus, large data sets such as that related to Web relevance ranking can be processed. The FPGA may include a processing element performing computations of a hidden layer of the neural network training algorithm. Parallel computing may be realized using a single instruction multiple data streams (SIMD) architecture with multiple arithmetic logic units in the FPGA.

A system and method for high-speed, low-power cryogenic computing are presented, comprising ultrafast energy-efficient RSFQ superconducting computing circuits, and hybrid magnetic/superconducting memory arrays and interface circuits, operating together in the same cryogenic environment. An arithmetic logic unit and register file with an ultrafast asynchronous wave-pipelined datapath is also provided. The superconducting circuits may comprise inductive elements fabricated using both a high-inductance layer and a low-inductance layer. The memory cells may comprise superconducting tunnel junctions that incorporate magnetic layers. Alternatively, the memory cells may comprise superconducting spin transfer magnetic devices (such as orthogonal spin transfer and spin-Hall effect devices). Together, these technologies may enable the production of an advanced superconducting computer that operates at clock speeds up to 100 GHz.

A circuit arrangement for the fault tolerant execution of digital computer programs includes a plurality of arithmetic logic units embodied as processor pool elements connected together so that they can each execute the program in parallel. The processor elements are connected to each other through respective data, clock and reset cross-strapping interconnect lines, and are each connected to one or more serial field buses. Each processor element includes at least one microprocessor controller for controlling the functions of the processor element in such a manner that any selected number of the processor elements can be automatically actuated at any time to simultaneously execute the program in parallel and thereby achieve a prescribed degree of redundancy in the circuit arrangement. The data cross-strapping line transmits data among the several processor elements, the clock signal cross-strapping line achieves a compelled synchronization of all of the processor elements, and the reset cross-strapping line carries out the deactivation of any processor element that is recognized as carrying out a faulty execution of the program or that is not necessary for achieving the required degree of redundancy. A deactivated processor element may later be reactivated to again participate in the parallel execution of the program.

A memory storage system is that includes a memory element having a memory address for a physical memory. A memory controller receives a command for accessing the memory element using a program-generated address and dynamically transforms the program-generated address into the memory address for the physical memory using a rotation module and configuration information. A data word accessed by the physical address is then provided to a set of arithmetic logic units (ALUs) where multiple computations are performed simultaneously so as top reduce program execution time and energy. The configuration information provided to the rotation unit configures the set of ALUs.

Methods and systems for residue number system based ALUs, processors, and other hardware provide the full range of arithmetic operations while taking advantage of the benefits of the residue numbers in certain operations. In one or more embodiments, an RNS ALU or processor comprises a plurality of digit slices configured to perform modular arithmetic functions. Operation of the digit slices may be controlled by a controller. Residue numbers may be converted to and from fixed or mixed radix number systems for internal use and for use in various computing systems.

An apparatus with circuit redundancy includes a set of parallel arithmetic logic units (ALUs), a redundant parallel ALU, input data shifting logic that is coupled to the set of parallel ALUs and that is operatively coupled to the redundant parallel ALU. The input data shifting logic shifts input data for a defective ALU, in a first direction, to a neighboring ALU in the set. When the neighboring ALU is the last or end ALU in the set, the shifting logic continues to shift the input data for the end ALU that is not defective, to the redundant parallel ALU. The redundant parallel ALU then operates for the defective ALU. Output data shifting logic is coupled to an output of the parallel redundant ALU and all other ALU outputs to shift the output data in a second and opposite direction than the input shifting logic, to realign output of data for continued processing, including for storage or for further processing by other circuitry.

In a method for displaying information in a motor vehicle, a plurality of objects are represented graphically with the aid of a display mounted in a motor vehicle. A user-interface device generates graphics data which control the display such that the graphical objects are shown disposed on a virtual, perspectively displayed ring, and that in response to an input with the aid of an input device, an arithmetic logic unit of the user-interface device alters the graphics data such that the graphical objects on the virtual ring rotate like a carousel. A corresponding display device is provided.

The present invention provides an application specific integrated circuit and a method of operation thereof. In one advantageous embodiment, the application specific integrated circuit includes a programmable logic core having an array of dynamically configurable arithmetic logic units. This particular embodiment further includes a network interface subsystem that includes a media access controller. The network interface is configured to employ a first portion of the programmable logic core that interfaces with the media access controller and that is configurable to process control data. This embodiment further includes a data transmission subsystem associated with a memory device, and configured to employ a second portion of the programmable logic core that stores received data from the network interface subsystem to the memory device and sends transmission data from the memory device to the network interface subsystem in response to an instruction from a host system.

The invention discloses a vector processor and a vector data access and interaction method thereof. The vector processor comprises an address register set, an address generation logic unit and a data interaction logic unit, wherein the address register set is used for storing an operand and vectorized address data and transmitting the vectorized address data to a memory set through an address bus interface, and the vectorized address data are used for accessing the memory set; the address generation logic unit is used for calculating the vectorized address data according to the operand through an arithmetic logic unit and storing the vectorized address data in the address register set; the data interaction logic unit is used for carrying out splitting/splicing operation on vector data, which are accessed from the memory set through the address bus interface, through the arithmetic logic unit. According to the vector processor and the vector data access and interaction method thereof, the efficient arithmetic logic unit of the vector processor can be utilized sufficiently, so that the data processing speed is high and the efficiency is high.

An assistance system that optimizes the operation of a self-propelled agricultural working machine includes a device for determining working and efficiency parameters of the machine includes an arithmetic logic unit and a display unit. The arithmetic logic unit processes information generated by machine-internal sensor systems, external information and information stored in the arithmetic logic unit. One or more mathematical models describing the working process are stored in the arithmetic logic unit and derive efficiency parameters of the working machine from the available working parameters and, with consideration for monetary interrelationships, determine the opportunity costs of the working process and visualize the opportunity costs in the display unit.