Cluster Restoration-Based Trace Signal Selection for Post-Silicon Debug

With the fabrication technology fast approaching 7nm, post-silicon validation has become an integral part of integrated circuit design to capture and eliminate functional bugs that escape pre-silicon validation. The major roadblock in post-silicon functional verification is limited observability of internal signals in a design. A possible solution to address this roadblock is to make use of embedded memories on chip called trace buffers. The amount of debug data that can be acquired from the trace buffer depends on its width and depth. The width of the trace buffer limits the number of signals that can be traced and the depth of the trace buffer limits the number of samples that can be acquired. Using the acquired data from the trace buffer, the values of other nodes in the circuit can be reconstructed. These trace buffers have limited area, hence only a few critical signals can be recorded by it. In this work we used the simulated annealing heuristic to select trace signals. We developed this idea from the fact that trace signal selection can be viewed as a bi-partitioning problem, the set of flip-flops being tapped onto the trace buffer is one partition and remaining flip-flops form the other partition. Experimental results demonstrate that our approach can result in better restoration ratio compared to the state-of-the-art techniques.

Today's complex integrated circuit designs increasingly rely on post-silicon validation to eliminate bugs that escape from pre-silicon verification. One effective silicon debug technique is to monitor and trace the behaviors of the circuit during its normal operation. However, designers can only afford to trace a small number of signals in the design due to the associated overhead. Selecting which signals to trace is therefore a crucial issue for the effectiveness of this technique. This paper proposes an automated trace signal selection strategy that is able to dramatically enhance the visibility in post-silicon validation. Experimental results on benchmark circuits show that the proposed technique is more effective than existing solutions.

We experimentally demonstrate a controlled-phase gate for continuous variables using a cluster-state resource of four optical modes. The two independent input states of the gate are coupled with the cluster in a teleportation-based fashion. As a result, one of the entanglement links present in the initial cluster state appears in the two unmeasured output modes as the corresponding entangling gate acting on the input states. The genuine quantum character of this gate becomes manifest and is verified through the presence of entanglement at the output for a product two-mode coherent input state. By combining our gate with the recently reported module for single-mode Gaussian operations [R. Ukai et al., Phys. Rev. Lett. 106, 240504 (2011)], it is possible to implement any multimode Gaussian operation as a fully measurement-based one-way quantum computation.

In this paper, we propose a verifiable quantum cloud computation scheme based on blind quantum computation which effectively solves the privacy and verifiability problem for clients when interfacing with quantum computer in cloud computation. Different from most of present works (e.g. using brickwork state as resource state), we use cluster state with grid distributed structure to realize the universal and cryptographic calculation which is more suitable for the distributed architecture of cloud computation. Throughout the process, the random key is always held by the client that effectively promote the confidentiality. Moreover, we propose a new method to make the client Alice verifies the correctness of the results through only classical calculation. Security analysis shows that cloud server Bob can only obtain the dimension of input state and cluster state from the calculation, but cannot obtain any information of the input, the output and the algorithm. Dishonest Bob will be detected during the verification.

Lack of observability combined with a huge design complexity make post-silicon validation and debug challenging. Assertions, as well as a wide variety of checkers, are used in pre-silicon stage to monitor specific functional scenarios. To improve the observability during post-silicon debug, pre-silicon assertions can be synthesized to coverage monitors and placed on silicon. However, synthesizing thousands of assertions can introduce unacceptable area and energy overhead. On the other hand, the absence of coverage monitors would negatively impact post-silicon coverage analysis to capture the coverage of specific events. This chapter proposes a framework for cost-effective post-silicon coverage analysis by identifying hard-to-detect assertions. The approach is based on utilizing existing debug infrastructure to enable coverage analysis in the absence of synthesized coverage monitors. This analysis allows us to identify a small percentage of coverage monitors that need to be synthesized to provide a trade-off between observability versus design overhead. To improve the observability further, an observability-aware trace signal selection algorithm is proposed to give priority to signals associated with important coverage monitors.

Post-silicon validation is a major challenge due to the combined effects of debug complexity and observability constraints. Assertions as well as a wide variety of checkers are used in pre-silicon stage to monitor certain functional scenarios. Pre-silicon checkers can be synthesized to coverage monitors in order to capture the coverage of certain events and improve the observability during post-silicon debug. Synthesizing thousands of coverage monitors can introduce unacceptable area and energy overhead. On the other hand, absence of coverage monitors would negatively impact post-silicon coverage analysis. In this paper, we propose a framework for cost-effective post-silicon coverage analysis by identifying hard-to-detect events coupled with trace-based coverage analysis. This paper makes three major contributions. We propose a method to utilize existing debug infrastructure to enable coverage analysis in the absence of synthesized coverage monitors. This analysis enables us to identify a small percentage of coverage monitors that need to be synthesized in order to provide a trade-off between observability and design overhead. To improve the observability further, we also present an observability-aware trace signal selection algorithm that gives priority to signals associated with important coverage monitors. Our experimental results demonstrate that an effective combination of coverage monitor selection and trace analysis can maintain the debugging observability with drastic reduction (up to 10 times) in the required coverage monitors.

Signal selection is the pre-silicon evaluation step which maximizes overall reconstruction rate of state elements. Due to its high complexity, recent efforts on signal selection has focused on sequential nodes in the circuit under debug. In this paper we propose a combinational signal selection algorithm which possibly selects combinational nodes of the circuit to maximize state restoration capability and achieve significant improvements (53% on average) compared to the state-of-the-art signal selection algorithms. To compensate for increase in problem complexity, we also propose a fast state restoration algorithm which offers significant improvement (38% on average) on simulation time over the state-of-the-art state restoration algorithms.

Reconstructing system-level behavior from silicon traces is a critical problem in post-silicon validation of System-on-Chip designs. Current industrial practice in this area is primarily manual, depending on collaborative insights of the architects, designers, and validators. This paper presents a trace analysis approach that exploits architectural models of system-level protocols to reconstruct design behavior from partially observed silicon traces in the presence of ambiguous and noisy data. The output of the approach is a set of all potential interpretations of a system's internal execution abstracted to system-level protocols. To support the trace analysis approach, a companion trace signal selection framework guided by system-level protocols is also presented, and its impacts on the complexity and accuracy of the analysis approach are discussed. That approach and the framework have been evaluated on a multi-core System-on-Chip prototype that implements a set of common industrial system-level protocols.

The most expensive task in modern design methodology is Post-silicon validation. As per the International Technology Roadmap for Semiconductors, time to market is the major limitation for verification and validation techniques. Functional verification is one of the major tasks in pre-silicon verification. The errors that are escaped from pre-silicon should be eliminated by post-silicon phase. The primary problem governing in silicon verification is that it has limited visibility of internal signals. This can be improved by the proposed merging concept of flip-flops which gives high observability with maximum restoration.

Global optimization of mechanical design problems using heuristic methods such as Simulated annealing (SA) and genetic algorithms (GAs) have been able to find global or near-global minima where prior methods have failed. The use of these nongradient based methods allow the broad efficient exploration of multimodal design spaces that could be continuous, discrete or mixed. From a survey of articles in the ASME Journal of Mechanical Design over the last 10 years, we have observed that researchers will typically run these algorithms in continuous mode for problems that contain continuous design variables. What we suggest in this paper is that computational efficiencies can be significantly increased by discretizing all continuous variables, perform a global optimization on the discretized design space, and then conduct a local search in the continuous space from the global minimum discrete state. The level of discretization will depend on the complexity of the problem, and becomes an additional parameter that needs to be tuned. The rational behind this assertion is presented, along with results from four test problems.

Target search via Bayesian estimation is a commonly studied problem in which the goal is to locate and possibly characterize a lost “target” within a given space using noisy sensors. Example applications include locating gas leaks, radio sources, ships at sea, etc, using appropriate sensors for the specific phenomena. The motivating application for this effort is detecting and locating nearby radioactive material in nuclear facilities. Past work has produced several implementations for addressing this problem, with varying levels of accuracy and sophistication. Here we present both new and previously developed solution methods to the radioactive source localization problem. The solutions have diverse strategies for selecting advantageous positions at which to collect measurements. The objective is to minimize the number of measurements needed to complete the target localization. We empirically compare the effectiveness of various strategies, three of which are novel and prove superior over prior methods of measurement selection. The relative advantages of the novel strategies are discussed as well as their broader applicability to a general class of target search problems where measurement intensity correlates with target proximity. Note to Practitioners—This paper was motivated by the desire to automate tasks in radiation work spaces of nuclear facilities, particularly laboratory facilities such as those at Los Alamos National Laboratory. Prior work by the authors studied the problem of autonomously locating and characterizing radiation sources in a survey region. This paper presents enhancements to the prior method, which reduce the number of radiation measurements needed to produce the solution. This increases the efficiency and practicality of the method for end users.

Selection is the first step in many image editing processes, enabling faster and simpler modifications of all pixels sharing a common modality. In this work, we present a method for material selection in images, robust to lighting and reflectance variations, which can be used for downstream editing tasks. We rely on vision transformer (ViT) models and leverage their features for selection, proposing a multi-resolution processing strategy that yields finer and more stable selection results than prior methods. Furthermore, we enable selection at two levels: texture and subtexture, leveraging a new two-level material selection (DuMaS) dataset which includes dense annotations for over 800,000 synthetic images, both on the texture and subtexture levels.

Post-silicon validation and debug have gained importance in recent years to track down errors that have escaped the pre-silicon phase. Limited observability of internal signals during post-silicon debug necessitates the storage of signal states in real time. Trace buffers are used to store these states. To increase the debug observation window, it is essential to compress these trace signals, so that trace data over larger number of cycles can be stored in the trace buffer while keeping its size constant. In this paper, we propose several dictionary based compression techniques for trace data compression that takes account of the fact that the difference between golden and erroneous trace data is small. Therefore, the static dictionary selected based on golden trace data can provide notably better compression performance than the dynamic dictionaries selected in the current approaches. This will also significantly reduce the hardware overhead by reducing the dictionary size. Our experimental results demonstrate that our approach can provide up to 60% better compression compared to existing approaches, while reducing the architecture overhead by 84%.

Cognitive behavioural therapy (CBT) is a psychosocial treatment that aims to re-mediate distressing emotional experiences or dysfunctional behaviour by changing the way in which a person interprets and evaluates the experience or cognates on its consequence and meaning. This approach helps to link the person's feelings and patterns of thinking which underpin distress. CBT is now recommended by the National Institute for Health and Care Excellence (NICE) as an add-on treatment for people with a diagnosis of schizophrenia. This review is also part of a family of Cochrane CBT reviews for people with schizophrenia. To assess the effects of cognitive behavioural therapy added to standard care compared with standard care alone for people with schizophrenia. We searched the Cochrane Schizophrenia Group's Trials Register (up to March 6, 2017). This register is compiled by systematic searches of major resources (including AMED, BIOSIS CINAHL, Embase, MEDLINE, PsycINFO, PubMed, and registries of clinical trials) and their monthly updates, handsearches, grey literature, and conference proceedings, with no language, date, document type, or publication status limitations for inclusion of records into the register. We selected all randomised controlled clinical trials (RCTs) involving people diagnosed with schizophrenia or related disorders, which compared adding CBT to standard care with standard care given alone. Outcomes of interest included relapse, rehospitalisation, mental state, adverse events, social functioning, quality of life, and satisfaction with treatment.We included studies fulfilling the predefined inclusion criteria and reporting useable data. We complied with the Cochrane recommended standard of conduct for data screening and collection. Where possible, we calculated relative risk (RR) and its 95% confidence interval (CI) for binary data and mean difference (MD) and its 95% confidence interval for continuous data. We assessed risk of bias for included studies and created a 'Summary of findings' table using GRADE. This review now includes 60 trials with 5,992 participants, all comparing CBT added to standard care with standard care alone. Results for the main outcomes of interest (all long term) showed no clear difference between CBT and standard care for relapse (RR 0.78, 95% CI 0.61 to 1.00; participants = 1538; studies = 13, low-quality evidence). Two trials reported global state improvement. More participants in the CBT groups showed clinically important improvement in global state (RR 0.57, 95% CI 0.39 to 0.84; participants = 82; studies = 2 , very low-quality evidence). Five trials reported mental state improvement. No differences in mental state improvement were observed (RR 0.81, 95% CI 0.65 to 1.02; participants = 501; studies = 5, very low-quality evidence). In terms of safety, adding CBT to standard care may reduce the risk of having an adverse event (RR 0.44, 95% CI 0.27 to 0.72; participants = 146; studies = 2, very low-quality evidence) but appears to have no effect on long-term social functioning (MD 0.56, 95% CI -2.64 to 3.76; participants = 295; studies = 2, very low-quality evidence, nor on long-term quality of life (MD -3.60, 95% CI -11.32 to 4.12; participants = 71; study = 1, very low-quality evidence). It also has no effect on long-term satisfaction with treatment (measured as 'leaving the study early') (RR 0.93, 95% CI 0.77 to 1.12; participants = 1945; studies = 19, moderate-quality evidence). Relative to standard care alone, adding CBT to standard care appears to have no effect on long-term risk of relapse. A very small proportion of the available evidence indicated CBT plus standard care may improve long term global state and may reduce the risk of adverse events. Whether adding CBT to standard care leads to clinically important improvement in patients' long-term mental state, quality of life, and social function remains unclear. Satisfaction with care (measured as number of people leaving the study early) was no higher for participants receiving CBT compared to participants receiving standard care. It should be noted that although much research has been carried out in this area, the quality of evidence available is poor - mostly low or very low quality and we still cannot make firm conclusions until more high quality data are available.

Fourier analysis of the R. Davis group data has been carried out. A method of signal selection at small signal/noise ratio was suggested and significance levels of individual peaks in the spectrum were determined. The correlation coefficients of neutrino fluxes and some characteristics of solar activity were calculated. The analysis conducted allowed us to reveal three significant peaks in the spectral density of 37Ar production rate in Cl detector: 113.8, 55.6, and 26.6 months (with a ratio of ∼ 4∶2∶1). The probability of a random origin of these peaks is less than 4%.