Synopsys, Inc.

Shekaripuram V Venkatesh

Solaiman Rahim

Vaibhav Jain

Sanjay Gulati

Chaofan Wang

Nitin Sharma

Fahim Rahim

Manish Goel

Siddharth Guha

Parul Bhatia

Sean Safarpour

Vishal Keswani

Tonatiuh Rangel Gordillo

A configuration may identify an IC chip component the IC chip component comprising one of a logic block, a memory block, and a power grid. A configuration may train a machine learning model based on one or more features and one or more labels corresponding to the identified IC chip component. A configuration may generate an artificial intelligence model having characteristics comprising the trained machine learning model, the one or more features, and the one or more labels. A configuration may generate a prediction for the one or more labels based on past, present and projected one or more features. A configuration may monitor future label prediction versus a failure threshold. A configuration may generate a notification in response to the failure threshold being reached.

Failure prediction of field-deployed mission critical integrated circuit chips using artificial intelligence

A method includes generating a plurality of input vectors based on input signals to an electric circuit, selecting a subset of the plurality of input vectors, and determining a plurality of datapoints based on the selected subset of the plurality of input vectors. Each datapoint of the plurality of datapoints indicates a power consumption of the electric circuit corresponding to an input vector of the selected subset of the input vectors. The method also includes generating, by a processor, a plurality of vector sequences based on the selected subset of the plurality of input vectors. Each vector sequence of the plurality of vector sequences includes a portion of the selected subset of the plurality of input vectors arranged chronologically. The method further includes training a machine learning model based on a first subset of the plurality of vector sequences and a corresponding first subset of the plurality of datapoints.

Power estimation using input vectors and deep recurrent neural networks

A method includes generating a plurality of vector sequences based on input signals of an electric circuit design and encoding the plurality of vector sequences. The method also includes clustering the plurality of encoded vector sequences into a plurality of clusters and selecting a set of encoded vector sequences from the plurality of clusters. The method further includes selecting a first set of vector sequences corresponding to the selected set of encoded vector sequences, selecting a second set of vector sequences from the plurality of vector sequences not in the first set of encoded vector sequences, and training, by a processing device, a machine learning model to predict power consumption using the first and second sets of vector sequences.

Selecting a subset of training data from a data pool for a power prediction model

A hierarchical power verification system and method creates abstract models of power behavior of modules that it successfully verifies. The abstract models simplify the module definition by omitting internal module details but provide sufficient information for power verification of higher level modules that incorporate this abstracted module. Design blocks are replaced with these abstract power models, resulting in reduced run-time and memory requirements. The power models can include power switches inside the block, related supplies of logic ports, supply power states, system power states, power management devices such as isolation logic and level shifters, feed-through and floating ports. The power model may be expressed either in UPF or as a combination of liberty model and UPF. After replacing modules with abstracted models the HPVS can quickly verify an entire SoC with a small memory footprint. When a user modifies a module, the HPVS need only verify the changed module and related modules at higher levels of module hierarchy.

System and method for hierarchical power verification

A power verification system requires a combination of design and its power intent. A power intent (PI) input specifies the power architecture of a design through specification of power/voltage domains, their corresponding power supplies and a collection of power management devices. Power state tables (PSTs) specified in PI capture the legal combinations of power states (voltage values) for the various sets of supply nets or supply ports of a design. A power verification system requires determining the power supply relationships of voltage/power domains which requires merging of PSTs. The system described efficiently merges PSTs by iteratively selecting only a subset of PSTs that are relevant to the supply pair of interest, that are pruned initially and as the merge progresses. This provides orders of magnitude speedup and resource reduction. A user interface allows display of identified power verification failures and may include an input device for facilitating correction of at least one of the electronic circuit design and the power intent file.

System and method for power verification using efficient merging of power state tables

A system and methods provide for an analysis of at least a portion of an integrated circuit (IC) that comprises a plurality of modules, for the purpose of identifying signals that can be indicative of the activity of the modules. By analyzing the activity of these signal immediately before and immediately after each module going from non-idle to idle and from idle to non-idle respectively, it is possible to determine which signals provide an indication that the module should be shut down. If the module can be shut down in idle state, then these input signals may be used as control signals for this purpose. By reporting to a designer the role of such signals a simple design change for detecting the activity and controlling the module, can save on power consumption, in ways not previously detected by the designer.

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Los Altos, CA, United States of America

Shekaripuram V Venkatesh