Design space exploration in logic synthesis and parameter tuning in physical design require a massive amount of compute resources in order to meet tapeout schedules. To address this need, cloud computing provides semiconductor and electronics companies with instant access to scalable compute resources. However, deploying electronic design automation (EDA) jobs on the cloud requires EDA teams to deeply understand the characteristics of their jobs in cloud environments. Unfortunately, there has been little to no public information on these characteristics. Thus, in this article, we first formulate the problem of moving EDA jobs to the cloud. To address the problem, we characterize the performance of four EDA main applications, namely: 1) synthesis; 2) placement; 3) routing; and 4) static timing analysis. We show that different EDA jobs require different compute configurations in order to achieve the best performance. Using observations from our characterization, we propose a novel model based on graph convolutional networks to predict the total runtime of a given stage on different configurations. Our model achieves a prediction accuracy of 87%. Furthermore, we present a new formulation for optimizing cloud deployments in order to reduce costs while meeting deadline constraints. We present a pseudopolynomial optimal solution using a multichoice knapsack mapping that reduces deployment costs by 35.29%, with minimal overhead to the total runtime. In addition, we describe a cloud-ready solution, called EDA analytics central, for the continuous optimization of a design across an EDA flow. We used this system in building our runtime prediction model.

Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In this work, we propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention. We demonstrate the training of an Advantage Actor Critic (A2C) agent that seeks to minimize area subject to a timing constraint. Using the proposed methodology, designs can be optimized autonomously with no-humans in-loop. Evaluation on the comprehensive EPFL benchmark suite shows that the agent outperforms existing exploration methodologies and improves QoRs by an average of 13%.

We describe the planned Alpha release of OpenROAD, an open-source end-to-end silicon compiler. OpenROAD will help realize the goal of "democratization of hardware design", by reducing cost, expertise, schedule and risk barriers that confront system designers today. The development of open-source, self-driving design tools is in and of itself a "moon shot" with numerous technical and cultural challenges. The open-source flow incorporates a compatible open-source set of tools that span logic synthesis, floorplanning, placement, clock tree synthesis, global routing and detailed routing. The flow also incorporates analysis and support tools for static timing analysis, parasitic extraction, power integrity analysis, and cloud deployment. We also note several observed challenges, or "lessons learned", with respect to development of open-source EDA tools and flows.

BACKGROUND: Due to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data's noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm. RESULTS: To investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments' breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates. CONCLUSIONS: In this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

BACKGROUND: Recently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer. RESULTS: In this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly. CONCLUSIONS: The limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

In this paper, we introduce xGame – a game engine for mobile audio games. The xGame engine not only offers an open source framework for building screen reader friendly games, but also introduces a new approach for developers to design and develop audio games. Using the developed game engine, we built an Android application that serves as a hub for all games developed using the engine. xGame boosts the development of audio games, hence fills the gap between blinds and sighted.

MOTIVATION: There is a growing need in bioinformatics for easy-to-use software implementations of algorithms that are usable across platforms. At the same time, reproducibility of computational results is critical and often a challenge due to source code changes over time and dependencies. RESULTS: The approach introduced in this paper addresses both of these needs with AlgoRun, a dedicated packaging system for implemented algorithms, using Docker technology. Implemented algorithms, packaged with AlgoRun, can be executed through a user-friendly interface directly from a web browser or via a standardized RESTful web API to allow easy integration into more complex workflows. The packaged algorithm includes the entire software execution environment, thereby eliminating the common problem of software dependencies and the irreproducibility of computations over time. AlgoRun-packaged algorithms can be published on http://algorun.org, a centralized searchable directory to find existing AlgoRun-packaged algorithms. AVAILABILITY AND IMPLEMENTATION: AlgoRun is available at http://algorun.org and the source code under GPL license is available at https://github.com/algorun CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.