Hybrid Verification for Analog and Mixed-signal Circuits

With increasing design complexity and reliability requirements, analog and mixedsignal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two types of techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by inherent errors in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machinelearning- based verification technique (HFMV) to combine the best of the two worlds. HFMV builds formalism on the top of a machine learning model to verify AMS circuits efficiently while meeting a user-specified confidence level. Guided by formal checks, HFMV intelligently explores the high-dimensional parameter space of a given design by iteratively improving the machine learning model. As a result, it leads to accurate failure prediction in the case of a failing circuit or a reliable pass decision in the case of a good circuit. Our experimental results demonstrate that the proposed HFMV approach is capable of identifying hard-to-find failures which are completely missed by a huge number of random simulation samples while significantly cutting down training sample size and verification cycle time

Hybrid Verification for Analog and Mixed-signal Circuits

With increasing design complexity and reliability requirements, analog and mixedsignal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two types of techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by inherent errors in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machinelearning- based verification technique (HFMV) to combine the best of the two worlds. HFMV builds formalism on the top of a machine learning model to verify AMS circuits efficiently while meeting a user-specified confidence level. Guided by formal checks, HFMV intelligently explores the high-dimensional parameter space of a given design by iteratively improving the machine learning model. As a result, it leads to accurate failure prediction in the case of a failing circuit or a reliable pass decision in the case of a good circuit. Our experimental results demonstrate that the proposed HFMV approach is capable of identifying hard-to-find failures which are completely missed by a huge number of random simulation samples while significantly cutting down training sample size and verification cycle time

Metabolomics–Proteomics Combined Approach Identifies Differential Metabolism-Associated Molecular Events between Senescence and Apoptosis

Apoptosis and senescence are two types of cell fates in response to chemotherapy. Besides canonical pathways that mediate cell fates, cancer cell metabolism has been revealed as a crucial factor affecting cell fate decisions and thus represents a new target for antitumor therapy. Therefore, a comprehensive description of metabolic pathways underlying cell senescence and apoptosis in response to chemotherapy is highly demanded for therapeutic exploitation of both processes. Herein we employed a metabolomics–proteomics combined approach to identify metabolism-associated molecular events that mediate cellular responses to senescence and apoptosis using doxorubicin-treated human breast cancer cells MCF7 as models. Such biomics approach revealed that tricarboxylic acid cycle, pentose phosphate pathway, and nucleotide synthesis pathways were significantly upregulated in the senescent model, whereas fatty acid synthesis was reduced. In apoptotic cells, an overall reduced activity of major metabolic pathways was observed except for the arginine and proline pathway. Combinatorially, these data show the utility of biomics in exploring biochemical mechanism-based differences between apoptosis and senescence and reveal an unprecedented finding of the metabolic events that were induced for survival by facilitating ROS elimination and DNA damage repair in senescent cells, while they were downregulated in apoptotic cells when DNA damage was irreparable