83 results about "Hybrid computation" patented technology

The Field Programmable Instrument Controller (FPIC) is a stand-alone low to high performance, clocked or unclocked multi-processor that operates as a microcontroller with versatile interface and operating options. The FPIC can also be used as a concurrent processor for a microcontroller or other processor. A tightly coupled Multiple Chip Module design incorporates non-volatile memories, a large field programmable gate array (FPGA), field programmable high precision analog to digital converters, field programmable digital to analog signal generators, and multiple ports of external mass data storage and control processors. The FPIC has an inherently open architecture with in-situ reprogrammability and state preservation capability for discontinuous operations. It is designed to operate in multiple roles, including but not limited to, a high speed parallel digital signal processing; co-processor for precision control feedback during analog or hybrid computing; high speed monitoring for condition based maintenance; and distributed real time process control. The FPIC is characterized by low power with small size and weight.

A hybrid system-on-chip provides a plurality of memory and processor die mounted on a semiconductor carrier chip that contains a fully integrated power management system that switches DC power at speeds that match or approach processor core clock speeds, thereby allowing the efficient transfer of data between off-chip physical memory and processor die.

An integrated hybrid system is provided. The hybrid system includes compute components of different types and architectures that are integrated and managed by a single point of control to provide federation and the presentation of the compute components as a single logical computing platform.

Executing a service program for an accelerator application program in a hybrid computing environment that includes a host computer and an accelerator, the host computer and the accelerator adapted to one another for data communications by a system level message passing module; where the service program includes a host portion and an accelerator portion and executing a service program for an accelerator includes receiving, from the host portion, operating information for the accelerator portion; starting the accelerator portion on the accelerator; providing, to the accelerator portion, operating information for the accelerator application program; establishing direct data communications between the host portion and the accelerator portion; and, responsive to an instruction communicated directly from the host portion, executing the accelerator application program.

The present disclosure relates to a processor for implementing artificial neural networks, for example, convolutional neural networks. The processor includes a memory controller group, an on-chip bus and a processor core, wherein the processor core further includes a register map, an instruction module, a data transferring controller, a data writing scheduling unit, a buffer module, a convolution operation unit and a hybrid computation unit. The processor of the present disclosure may be used for implementing various neural networks with increased computation efficiency.

A computer system architecture and memory controller for close-coupling within a hybrid computing system using an adaptive processor interface port (“APIP”) added to, or in conjunction with, the memory and I/O controller chip of the core logic. Memory accesses to and from this port, as well as the main microprocessor bus, are then arbitrated by the memory control circuitry forming a portion of the controller chip. In this fashion, both the microprocessors and the adaptive processors of the hybrid computing system exhibit equal memory bandwidth and latency. In addition, because it is a separate electrical port from the microprocessor bus, the APIP is not required to comply with, and participate in, all FSB protocol. This results in reduced protocol overhead which results higher yielded payload on the interface.

Systems and methods are described for operating a hybrid computing system using cluster contraction for converting large, dense input to reduced input that can be easily mapped into a quantum processor. The reduced input represents the global structure of the problem. Techniques involve partitioning the input variables into clusters and contracting each cluster. The input variables can be partitioned using an Unweighted Pair Group Method with Arithmetic Mean algorithm. The quantum processor returns samples based on the reduced input and the samples are expanded to correspond to the original input.

The invention provides a DNN task unloading method in an edge-cloud hybrid computing environment and a terminal. According to the types and the number of computing nodes, the number of DNN tasks to be unloaded and the number of layers of each DNN task to be unloaded, calculating the number of the DNN tasks to be unloaded; establishing a target function based on total cost minimization; determining a corresponding constraint condition. The influences of conditions such as computing power and time delay constraints of different types of nodes are considered, the feasibility of the obtained optimal solution is guaranteed, when the optimal solution is solved, crossover operation and mutation operation in the genetic algorithm are introduced into the particle swarm algorithm, a specific algorithm is given, and the problem that the particle swarm algorithm is prone to falling into local optimum in the optimal solution solving process is effectively solved.

The invention relates to a hybrid computation method for acquiring few-group cross section parameters of a fast neutron reactor. The hybrid computation method comprises the following steps: combining a Monte Carlo method with a certain theory method, finely considering the resonance effect of a fast reactor assembly by utilizing the Monte Carlo method, calculating precise multi-group microcosmic total cross section, fission cross section and elastic scattering cross section of each nuclide, solving various orders of elastic scattering cross sections and various orders of neutron flux moments of the fast reactor assembly by utilizing the certain theory method, and aggregating the multi-group cross section into the few-group cross section. The hybrid computation method provided by the invention is strong in universality and wide in application range, a high-precision few-group cross section of the fast reactor assembly can be produced, and precise and reliable cross section parameters are provided for a task of designing a nucleus of a reactor core.

The invention provides an improved high-order nonlinear spatial discretization method for solving an Euler equation, and the method comprises the following steps: 1, reading initial flow field data, and calculating the positive and negative fluxes of each node at the moment of the Euler equation; 2, performing feature projection on the positive flux and the negative flux on each node to obtain a feature flux, and calculating an intermittent detection factor according to the feature flux on each node; 3, constructing a high-order hybrid calculation method of numerical flux on a half point according to the intermittent detection factor, and completing spatial discretization of the Euler equation; 4, discretizing the time items by adopting a three-order Runge-Kutta method; and 5, pushing thetime to a specified tN to finish calculation to obtain flow field data at the tN moment. According to the improved high-order nonlinear spatial discretization method, the WENN-LC format has higher flow structure resolution than a traditional NND format under the same grid; in addition, the hyWENN-LC mixed format not only has higher resolution, but also has higher calculation efficiency.

The invention discloses a RISC-V-based hybrid mixed computing system and method. The system comprises an instruction control module and a mixed calculation module, an expansion instruction is arrangedin the instruction control module, and the expansion instruction contains operation information; the instruction control module is used for automatically setting operation data and operation information in sequence and then transmitting the operation data and the operation information to the hybrid mixed calculation module through an extension instruction; and the hybrid calculation module is used for selecting a corresponding operation mode according to the operation information, performing hybrid operation by combining the operation data and the selected operation mode, and outputting an operation result. Complex hybrid operation is achieved through a single extension instruction, and some complex operations of the processor based on the RISC-V instruction set are simplified. The calculation process of complex hybrid computation is simplified; the disassembling machine code is simpler and clearer; time consumption caused by multiple times of cyclic operation is reduced, the system performance is improved, and the method can be widely applied to the technical field of communication.

The embodiment of the invention provides a hybrid computing system and a data processing method and device. The hybrid computing system comprises a classification layer and a computing layer, and thecomputing layer comprises a first computing engine based on batch processing and a second computing engine based on stream processing. The classification layer is used for obtaining features of the calculation task according to the first code corresponding to the calculation task and determining a target calculation engine according to the features. The classification layer is further used for converting the first code into a second code corresponding to a target computing engine and sending the second code to the target computing engine, so that the target computing engine executes a computing task to process the to-be-processed data. The hybrid computing system comprises the first computing engine based on batch processing and the second computing engine based on stream processing, so that the hybrid computing system is suitable for executing both batch processing tasks and stream processing tasks, and the applicability of the hybrid computing system is improved.

The invention discloses a neural network and swarm hybrid calculation method for intelligent environment carrier robot floor identification. After various acquired data are clustered according to weather modes, based on different weather modes, fluctuating pressure sensor readings are subjected to FIR filtering processing, the readings are then transmitted to a data analysis module for neural network learning, and the accuracy and the real-time performance of floor identification are greatly improved. The vibration problem of data acquired by a pressure sensor can be greatly improved, and the height data signal analysis precision is greatly improved; universal adaptability is realized, and the method can handle elevator floor identification under each altitude, each geographical location and each weather condition; and the method is not limited to be used by the carrier robot in the elevator, floor estimation in a passageway can also be carried out, and the method can also be applied to fields such as overhead operation and unmanned aerial vehicles.

A hybrid computing system comprising a quantum computer and a digital computer employs a digital computer to use machine learning methods for post-processing samples drawn from the quantum computer. Post-processing samples can include simulating samples drawn from the quantum computer. Machine learning methods such as generative adversarial networks (GANs) and conditional GANs are applied. Samples drawn from the quantum computer can be a target distribution. A generator of a GAN generates samples based on a noise prior distribution and a discriminator of a GAN measures the distance between the target distribution and a generative distribution. A generator parameter and a discriminator parameter are respectively minimized and maximized.

The invention discloses a multi-robot cloud control system based on a cloud side end hybrid computing environment. The multi-robot cloud control system comprises an execution module, a communication module, a knowledge base module, an intelligent algorithm module and a general control module; the execution module comprises a robot and a sensing device, and is responsible for acquisition and processing of sensing data, executing a control instruction; the communication module is responsible for forwarding and processing real-time and dynamic data in a cloud side end environment; the scene general knowledge base module is used for storing general knowledge involved in a scene and providing a unified calling interface; the intelligent algorithm module is responsible for providing an intelligent perception and autonomous decision algorithm required in the scene; and the general control module is responsible for arranging and scheduling robots in the scene to cooperatively complete a specified task. The multi-robot cloud control system is stable, efficient and high in fault tolerance.