The disclosed method is applicable to a many-core system. The method includes: acquiring multiple pieces of .routing information, each of which includes two logical nodes and a data transmission amount between the two logical nodes; determining a piece of unprocessed routing information with a maximum data transmission amount as current routing information; mapping each unlocked logical node of the current routing information to one unlocked processing node, and locking the mapped logical node and processing node, wherein if there is an unlocked edge processing node, the unlocked logical node is mapped to the unlocked edge processing node; and returning, if there is at least one unlocked logical node, to the step of determining the piece of unprocessed routing information with the maximum data transmission amount as the current routing information.

The disclosed method is applicable to a many-core system. The method includes: acquiring multiple pieces of .routing information, each of which includes two logical nodes and a data transmission amount between the two logical nodes; determining a piece of unprocessed routing information with a maximum data transmission amount as current routing information; mapping each unlocked logical node of the current routing information to one unlocked processing node, and locking the mapped logical node and processing node, wherein if there is an unlocked edge processing node, the unlocked logical node is mapped to the unlocked edge processing node; and returning, if there is at least one unlocked logical node, to the step of determining the piece of unprocessed routing information with the maximum data transmission amount as the current routing information.

Various examples are provided related to automated chip design, such as a pareto-optimization framework for automated network-on-chip design. In one example, a method for network-on-chip (NoC) design includes determining network performance for a defined NoC configuration comprising a plurality of n routers interconnected through a plurality of intermediate links; comparing the network performance of the defined NoC configuration to at least one performance objective; and determining, in response to the comparison, a revised NoC configuration based upon iterative optimization of the at least one performance objective through adjustment of link allocation between the plurality of n routers. In another example, a method comprises determining a revised NoC configuration based upon iterative optimization of at least one performance objective through adjustment of a first number of routers to obtain a second number of routers and through adjustment of link allocation between the second number of routers.

Various examples are provided related to automated chip design, such as a pareto-optimization framework for automated network-on-chip design. In one example, a method for network-on-chip (NoC) design includes determining network performance for a defined NoC configuration comprising a plurality of n routers interconnected through a plurality of intermediate links; comparing the network performance of the defined NoC configuration to at least one performance objective; and determining, in response to the comparison, a revised NoC configuration based upon iterative optimization of the at least one performance objective through adjustment of link allocation between the plurality of n routers. In another example, a method comprises determining a revised NoC configuration based upon iterative optimization of at least one performance objective through adjustment of a first number of routers to obtain a second number of routers and through adjustment of link allocation between the second number of routers.

A method, apparatus, computer device, and storage medium for automatic design of analog circuits based on tree structure. The method includes: setting the maximum height and growth direction of the tree structure; randomly calling the node from the function node library as the parent node; randomly calling the node from the function node library and the port node library as the child according to the growth direction node; if the child node is a terminal node, generating a tree structure; checking the tree structure, if the tree structure satisfies the preset conditions, obtaining the circuit topology and device parameter that conform to the circuit rules; evolving the circuit topology and device parameter to generate an analog circuit. The embodiments achieve the effect of making the tree structure of the designed analog circuit more reasonable.

A system and method are provided to enable non-quantum experts to schematically represent, simulate and quantify the performance of physically realistic photonic quantum circuits. The framework offers the flexibility for users—not necessarily familiar with the fundamentals of quantum mechanics—to create circuits and work with simple inputs and outputs, while the complexities of manipulating high dimensionality quantum Hilbert spaces supporting photonic and physical quantum object states are handled with the use of purpose-built tools. The tools include a user-friendly method for defining classical photonic circuits which may be coupled to physical objects such as qubits, quantum input states, as well as classical and quantum measurement devices. The tools feature classical-to-quantum S-matrix conversion, quantum S-matrix extraction, as well as capabilities for defining and extracting quantum error parameters. The framework also supports extraction of post-measurement quantum states for use in subsequent circuits or simulators.

A method for generating a model of a transistor includes: initializing hyper-parameters; training the neural network in accordance with the hyper-parameters and training data relating transistor input state values to transistor output state values to compute neural network parameters; determining whether the transistor output state values of the training data match an output of the neural network; porting the neural network to a circuit simulation code to generate a ported neural network; simulating a test circuit using the ported neural network to simulate behavior of a transistor of the test circuit to generate simulation output; determining whether a turnaround time of the generation of the simulation output is satisfactory; in response to determining that the turnaround time is unsatisfactory, re-training the neural network based on updated hyper-parameters; and in response to determining that the turnaround time is satisfactory, outputting the ported neural network as the model of the transistor.

A method for generating a model of a transistor includes: initializing hyper-parameters; training the neural network in accordance with the hyper-parameters and training data relating transistor input state values to transistor output state values to compute neural network parameters; determining whether the transistor output state values of the training data match an output of the neural network; porting the neural network to a circuit simulation code to generate a ported neural network; simulating a test circuit using the ported neural network to simulate behavior of a transistor of the test circuit to generate simulation output; determining whether a turnaround time of the generation of the simulation output is satisfactory; in response to determining that the turnaround time is unsatisfactory, re-training the neural network based on updated hyper-parameters; and in response to determining that the turnaround time is satisfactory, outputting the ported neural network as the model of the transistor.

A method for analyzing an electrical circuit. The method includes procuring one or multiple netlists(s) of the circuit; supplying the one or multiple netlist(s) to a classification model; and mapping, by the classification model, one or multiple of the connection(s) included in the netlist or netlists onto one or multiple net type(s) from a predefined selection of net types.

According to one embodiment, an integrated circuit verification device includes: a condition property information generation unit configured to generate a plurality of condition properties that have information which imposes limitations on circuit operations or input signals, based on condition statements in a code list of a design data file; an exclusion code generation unit configured to generate, from the code list, exclusion code which is proved not to be statically covered, and a first exclusion code list to which the plurality of condition properties are applied; and an exclusion code comparison unit configured to generate a second exclusion code list from a difference between the exclusion code and the first exclusion code list.