International Business Machines Corporation

Globalfoundries Inc.

Alexander Ivrii

Jason Raymond Baumgartner

Pradeep Kumar Nalla

Raj Kumar Gajavelly

Arie Matsliah

Robert Lowell Kanzelman

Ziv Nevo

Oded Fuhrmann

Tatyana Veksler

Ayesha Akhter

Haim Kermany

Lars W Liebmann

Hari Mony

Gustavo Enrique Tellez

Oded Margalit

Avi Ziv

Michael S Gray

Kevin W McCullen

Rachel Tzoref-Brill

Itai Segall

Sharon Keidar-Barner

Matthew Thomas Guzowski

Hana Chockler

Shiri Moran

Mihir Choudhury

Raviv Gal

Yael Ben-Haim

Michael Gester

Rohit Dureja

An example system includes a processor to receive a high-level design representation of a system architecture. The processor can synthesize a logic design and generate an associated synthesis history based on the high-level hardware design representation. The processor can then execute an equivalence check between the high-level design and the synthesized logic design based on the generated synthesis history.

Equivalence checking of synthesized logic designs using generated synthesis history

In an approach for optimization of integer arithmetic expressions implemented as a Boolean logic circuit, a processor converts arithmetic operators in an arithmetic expression into adders. A processor identifies a topological order of the adders. A processor merges the adders based on the topological order into a multi-operand adder. A processor converts the multi-operand adder to a compressor tree and a two-operand adder. A processor performs the arithmetic expression based on the converted multi-operand adder.

Optimization of arithmetic expressions

A method, and apparatus and a computer program product for determining coverage in hardware verification based on relations between coverage events. The method comprises generating an over-approximation model of the hardware being verified to perform formal verification thereof with respect to a target coverage event being utilized in the verification process along with a set of coverage events. A score indicating an estimated conditional probability to hit the target coverage event in the verification process, given that the coverage event is hit in the verification process, may be determined for each coverage event based on the formal verification. The method further comprises selecting test suits to be executed in the verification process based on the scores and the test suits probability to hit each coverage event. The verification process may be the performed the selected test suits in order to cover the target coverage event.

Hardware verification based on relations between coverage events

Embodiments of the invention are directed to a computer-implemented method of logic verification. The method includes obtaining a netlist of a circuit comprising a plurality of observable gates. A first observable gate is grouped together with a second observable gate based on a portion of a fan-in logic of the first observable gate being equal to a portion of a fan-in logic of the second observable gate. The group is expanded by including a third observable gate, based on a first strongly connected component (SCC) in the group having a similarity greater than a first threshold to a second SCC in the fan-in logic of the third observable gate. The group is further expanded by including a fourth observable gate, based on the distance of a portion of the fan-in logic of the fourth observable gate from a fan-in logic of at least one observable gate in the group of observable gates.

Grouping and partitioning of properties for logic verification

A logic verification program, method and system provide an efficient behavior when verifying large logic designs. The logic is partitioned by cut-nodes that dominate two or more RANDOMS and a check is performed for a given cut-node to determine whether any of the dominated RANDOMS can be merged to a constant by performing satisfiability checks with each RANDOM merged to a constant, to determine whether a range of output values for the given cut-node has been reduced by merging the RANDOM. If the range is not reduced, the RANDOM can be added to the set of merge-able RANDOMS along with the corresponding constant value. If the range has been reduced, the opposite constant value is tried for a node and if the range is reduced for both constants, then the cut-node is abandoned for merging that dominated RANDOM and the next dominated RANDOM is tried.

Verification complexity reduction via range-preserving input-to-constant conversion

A computerized method for mapping of electronic designs comprising using at least one hardware processor for receiving a first hardware design model and a second hardware design model, each hardware design model configured to receive a startup state and send digital output values. Hardware processor(s) are used for generating a plurality of initial states. Hardware processor(s) are used for computing, using each one of the first and second hardware design models, at least one specific output value for each one of the plurality of initial states. Hardware processor(s) are used for selecting corresponding initial states that produce equivalent at least one specific output value between the first hardware design model and the second hardware design model. Hardware processor(s) are used for storing the selected corresponding initial states as mappings between the first hardware design model and the second hardware design model.

Verifying sequential equivalence for randomly initialized designs

A computer implemented method for increasing scalability in bounded liveness verification includes receiving, by one or more processors, a counterexample trace showing a bounded liveness failure and a set of parameters associated with the counterexample trace, partitioning the counterexample trace into segments representing bound increments contributing to the bounded liveness failure, selecting, by one or more processors, a time interval during which to repeat input values, wherein the selected time interval correlates to one or more segments, evaluating, by one or more processors, the received counterexample after repeating the selected time interval, determining, by one or more processors, whether the evaluation indicates that the counterexample falsifies a deeper bound with respect to a bound or an unbounded liveness counterexample, and, responsive to determining the evaluation indicates that the counterexample falsifies a deeper bound, providing, by one or more processors, counterexample falsification results.

Scalable liveness verification

Embodiments herein describe a verification process that identifies unate primary inputs in input paths of a property gate. A property gate is logic inserted in a hardware design represented by a netlist which is used to verify the design. Before performing the verification process, a computing system evaluates the netlist to identify the primary inputs in the input paths of the property gate and whether these primary inputs are unate or binate. To do so, in one embodiment, the computing system sets the output of the property gate in an error state and then traverses the input paths of the property gate to identify the values of the logic in the inputs paths that would result in the property gate being in the error state. Based on these polarities, the system can identify the unate and binate primary inputs.

Scalable logic verification by identifying unate primary inputs

A trace of a bounded liveness failure of a system component is received, by one or more processors, along with fairness constraints and liveness assertion conditions. One or more processors generate randomized values for unassigned input values and register values, of the trace, and simulate traversal of each of a sequence of states of the trace. One or more processors determine whether traversing the sequence of states of the trace results in a repetition of a state, and responsive to determining that traversing the sequence of states of the trace does result in a repetition of a state, and the set of fairness constraints are asserted within the repetition of a state, and that the continuous liveness assertion conditions are maintained throughout the repetition of the state, a concrete counterexample of a liveness property of the system component is reported.

Lifting of bounded liveness counterexamples to concrete liveness counterexamples

A methods, apparatus and product for explaining partially illegal combinations in combinatorial models. The method comprising: obtaining a combinatorial model defining a legal test space, the combinatorial model comprising a set of attributes, a respective domain for each attribute defining possible values for the attribute, and a set of restrictions, wherein the restrictions define a combination of values of the attributes that are illegal and are excluded from the legal test case; obtaining a partially illegal combination defining value assignments to a portion of the attributes; automatically identifying an extension of the partially illegal combination, wherein the extension is excluded from the legal test space, wherein the extension can be modified to become legal by changing a portion of the value assignments defined by the partially illegal combination; and outputting the extension.

Explaining partially illegal combinations in combinatorial models

Methods of the present disclosure can include: using a computing device to perform actions including: applying a design rule check (DRC) on a proposed integrated circuit (IC) layout, wherein the DRC applies a set of restrictive design rules (RDRs) in response to the proposed IC layout being a contact area (CA) layout; computing a conflict graph for the proposed IC layout in response to one of the IC layout being a metal layer layout and the set of RDRs being satisfied; determining whether the IC layout is one of non-colorable, indeterminate, partially colorable, and fully colorable; and partially coloring the IC layout and identifying non-colorable nodes in response to the IC layout being indeterminate or partially colorable.

Reducing color conflicts in triple patterning lithography

A method, apparatus and product for determining invariants in a model. One exemplary embodiments is a computer-implemented method performed by a computerized device, comprising: obtaining a set of candidates of invariants with respect to a model, the model comprising a transition relation axiom and an initial axiom; for substantially each candidate, adding to the model a first auxiliary variable, the first auxiliary variable is defined to be implied from the candidate being held in a predetermined cycle; iteratively trying to prove an inductive step with respect to a subset of the candidates, wherein in response to determining that a candidate is not held inductively removing the candidate from the subset of the candidates, wherein determining which candidate is not held inductively is performed based on values of the first auxiliary variables.

Determining invariants in a model

Method, apparatus, and product for performing incremental formal verification. A computer-implemented method performed by a computerized device. The method comprises: obtaining invariants with respect to a first model; determining a portion of the invariants that are invariants with respect to a second model, and utilizing the portion of the invariants to check that the second model holds a property.

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Haifa, Israel

Alexander Ivrii