Abstract: Processing a neural network data flow graph having a set of nodes and a set of edges. An insertion point is determined for a memory reduction or memory restoration operation. The determination is based on computing tensor timing slacks (TTS) for a set of input tensors; compiling a candidate list (SI) of input tensors, from the set of input tensors, using input tensors having corresponding TTS values larger than a threshold value (thTTS); filtering the SI to retain input tensors whose size meets a threshold value (thS); and determining an insertion point for the operation using the SI based on the filtering. A new data flow graph is generated or an existing one is modified using this process.

Abstract: A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor sing a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

Abstract: A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor using a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

Abstract: Processing a neural network data flow graph having a set of nodes and a set of edges. An insertion point is determined for a memory reduction or memory restoration operation. The determination is based on computing tensor timing slacks (TTS) for a set of input tensors; compiling a candidate list (SI) of input tensors, from the set of input tensors, using input tensors having corresponding TTS values larger than a threshold value (thTTS); filtering the SI to retain input tensors whose size meets a threshold value (thS); and determining an insertion point for the operation using the SI based on the filtering. A new data flow graph is generated or an existing one is modified using this process.

Abstract: A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by a processor using a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

Abstract: A computer-implemented method and computing system are provided for failure prediction of a batch of manufactured objects. The method includes classifying, by, a processor sing a simulation, a set of samples with uniformly distributed parameter values, to generate sample classifications for the batch of manufactured objects. The method further includes determining, by the processor, a centroid of failing ones of the samples in the set, based on the sample classifications. The method also includes generating, by the processor, a new set of samples with a distribution around the centroid of the failing ones of the sample in the set. The method additionally includes populating, by the processor, a nearest neighbor vector space using the new set of samples. The method further includes classifying, by the processor, the new set of samples by performing a nearest neighbor search on the nearest neighbor vector space using a distance metric.

Abstract: One embodiment of a method for solving an input satisfiability instance includes searching a database for a stored satisfiability instance that matches the input satisfiability instance and outputting a solution to the input satisfiability instance. One embodiment of method for converting an input satisfiability instance into a standardized representation includes applying a plurality of syntactical simplification rules to the input satisfiability instance until no conditions of any of the plurality of syntactical simplification rules can be met, thereby producing a simplified instance, uniformly replacing each variable in the simplified instance with a unique, consecutively chosen even number, annotating each literal in the simplified instance to indicate whether the each literal is positive or negative, ordering all literals in the simplified instance, and ordering all clauses in the simplified instance to produce the standardized representation.

Abstract: One embodiment of a method for solving an input satisfiability instance includes searching a database for a stored satisfiability instance that matches the input satisfiability instance and outputting a solution to the input satisfiability instance. One embodiment of method for converting an input satisfiability instance into a standardized representation includes applying a plurality of syntactical simplification rules to the input satisfiability instance until no conditions of any of the plurality of syntactical simplification rules can be met, thereby producing a simplified instance, uniformly replacing each variable in the simplified instance with a unique, consecutively chosen even number, annotating each literal in the simplified instance to indicate whether the each literal is positive or negative, ordering all literals in the simplified instance, and ordering all clauses in the simplified instance to produce the standardized representation.

Abstract: A pointerless BDD package. A strict ordering is enforced on the BDD node identifiers and the advantageous consequences of that decision, such as a better memory locality of the nodes and faster unique table lookup, are reaped. The performance of a pointer based package appears to be exceeded, and reproducible results are attained across different platforms.

Abstract: A pointerless BDD package. A strict ordering is enforced on the BDD node identifiers and the advantageous consequences of that decision, such as a better memory locality of the nodes and faster unique table lookup, are reaped. The performance of a pointer based package appears to be exceeded, and reproducible results are attained across different platforms.