Characteristics of a standard logic cell, e.g., a random logic cell, are determined using an effective cell approximation. The effective cell approximation is smaller than the standard logic cell and represents the density of lines and spaces of the standard logic cell. The effective cell approximation may be produced based on a selected area from the standard logic cell and include the same non-periodic patterns as the selected area. The effective cell approximation, alternatively, may represent non-periodic patterns in the standard logic cell using periodic patterns having a same density of lines and spaces as found in the standard logic cell. A structure on the sample, such as a logic cell or a metrology target produced based on the effective cell approximation is measured to acquire data, which is compared to the data for the effective cell approximation to determine a characteristic of the standard logic cell.

Characteristics of a standard logic cell, e.g., a random logic cell, are determined using an effective cell approximation. The effective cell approximation is smaller than the standard logic cell and represents the density of lines and spaces of the standard logic cell. The effective cell approximation may be produced based on a selected area from the standard logic cell and include the same non-periodic patterns as the selected area. The effective cell approximation, alternatively, may represent non-periodic patterns in the standard logic cell using periodic patterns having a same density of lines and spaces as found in the standard logic cell. A structure on the sample, such as a logic cell or a metrology target produced based on the effective cell approximation is measured to acquire data, which is compared to the data for the effective cell approximation to determine a characteristic of the standard logic cell.

A method of operation of a computing system includes: providing a first cluster having a first kernel unit for managing a first reconfigurable hardware device; analyzing an application descriptor associated with an application; generating a first bitstream based on the application descriptor for loading the first reconfigurable hardware device, the first bitstream for implementing at least a first portion of the application; and implementing a first fragment with the first bitstream in the first cluster.

Systems and methods are disclosed that map quantum circuits to physical qubits of a quantum computer. Techniques are disclosed to generate a graph that characterizes the physical qubits of the quantum computer and to compute the resource requirements of each circuit of the quantum circuits. For each circuit, the graph is searched for a subgraph that matches the resource requirements of the circuit, based on a density matrix. Physical qubits, defined by the matching subgraph, are then allocated to the logical qubits of the circuit.

Systems and methods are disclosed that map quantum circuits to physical qubits of a quantum computer. Techniques are disclosed to generate a graph that characterizes the physical qubits of the quantum computer and to compute the resource requirements of each circuit of the quantum circuits. For each circuit, the graph is searched for a subgraph that matches the resource requirements of the circuit, based on a density matrix. Physical qubits, defined by the matching subgraph, are then allocated to the logical qubits of the circuit.

Matrix multipliers are computationally complex, and memory intensive algorithms used frequently in a variety of applications, such as deep-learning and scientific computations. Accelerating matrix multiplication involves an inter-play of algorithm-architecture co-design and context-specific design parameters. A performance optimizer intelligently arrives at the right combination of algorithm (203)-architecture specifications (201, 202) for the input design parameters that arrive during real-time for a target-specific design constraint. The run-time customization leads to optimal power-performance-area optimization.

Matrix multipliers are computationally complex, and memory intensive algorithms used frequently in a variety of applications, such as deep-learning and scientific computations. Accelerating matrix multiplication involves an inter-play of algorithm-architecture co-design and context-specific design parameters. A performance optimizer intelligently arrives at the right combination of algorithm (203)-architecture specifications (201, 202) for the input design parameters that arrive during real-time for a target-specific design constraint. The run-time customization leads to optimal power-performance-area optimization.

Disclosed are a reinforcement learning apparatus and a reinforcement learning method for optimizing the position of an object based on design data. The present disclosure may configure a learning environment based on design data of a user and generate the optimal position of a target object, installed around a specific object during a design or manufacturing process, through reinforcement learning using simulation.

Disclosed are a reinforcement learning apparatus and a reinforcement learning method for optimizing the position of an object based on design data. The present disclosure may configure a learning environment based on design data of a user and generate the optimal position of a target object, installed around a specific object during a design or manufacturing process, through reinforcement learning using simulation.

The present invention concerns a method and a tool for designing and validating a data flow system comprising a set of software and/or hardware actors (a.sub.i, a.sub.j) interconnected with each other by unidirectional communication channels (c.sub.i, c.sub.j), the tool comprising: —a modelling interface (11) configured to generate an instance of the system by specifying, in a formal manner, a real-time and reconfigurable data flow, the reconfiguration of the data flow being carried out dynamically by propagating reconfiguration data from one actor to another through the communication channels, —an analysis module (13) configured to prove a predetermined set of behavioral properties of the system by means of a static analysis of the instance, —a refinement interface (15) designed to allocate resources to the instance, thus establishing a configured instance, the allocation of resources being carried out in such a way that an implementation of the system complies with the configured instance, and —a conformity test module (17) configured to verify the conformity of the behaviour of an implementation of the system with respect to the configured instance.