209 research outputs found

    MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

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    Security of Systems on Chip

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In recent years, technology has started to evolve to become more power efficient, powerful in terms of processors and smaller in size. This evolution of electronics has led microprocessors and other components to be merged to form a circuit called System-on-Chip. If we are to make a vast and cursory comparison between SoC and microcontrollers, microprocessors, and CPUs; we would come to the conclusion of SoCs being a single chip, doing all the things the other components can do yet without needing any external parts. So SoCs are computers just by themselves. Furthermore, SoCs have more memory than microcontrollers in general. Being a computer just by themselves allows them also to become servers. Nowadays, an SoC may be regarded also as a Server-on-Chi

    Analyzing multiple conflicts in SAT: an experimental evaluation

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    Unit propagation and conflict analysis are two essential ingredients of CDCL SAT Solving. The order in which unit propagation is computed does not matter when no conflict is found, because it is well known that there exists a unique unit-propagation fixpoint. However, when a conflict is found, current CDCL implementations stop and analyze that concrete conflict, even though other conflicts may exist in the unit-propagation closure. In this experimental evaluation, we report on our experience in modifying this concrete aspect in the CaDiCaL SAT Solver and try to answer the question of whether we can improve the performance of SAT Solvers by the analysis of multiple conflicts.All authors are supported by grant PID2021-122830OB-C43, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF: A way of making Europe”Peer ReviewedPostprint (published version

    CadiBack: Extracting Backbones with CaDiCaL

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    The backbone of a satisfiable formula is the set of literals that are true in all its satisfying assignments. Backbone computation can improve a wide range of SAT-based applications, such as verification, fault localization and product configuration. In this tool paper, we introduce a new backbone extraction tool called CadiBack. It takes advantage of unique features available in our state-of-the-art SAT solver CaDiCaL including transparent inprocessing and single clause assumptions, which have not been evaluated in this context before. In addition, CaDiCaL is enhanced with an improved algorithm to support model rotation by utilizing watched literal data structures. In our comprehensive experiments with a large number of benchmarks, CadiBack solves 60% more instances than the state-of-the-art backbone extraction tool MiniBones. Our tool is thoroughly tested with fuzzing, internal correctness checking and cross-checking on a large benchmark set. It is publicly available as open source, well documented and easy to extend

    A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning

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    The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix multiplication (GEMM), leading to unnecessarily large area cost and high control complexity. Here, we move beyond classical GEMM-based ONNs and propose an optical subspace neural network (OSNN) architecture, which trades the universality of weight representation for lower optical component usage, area cost, and energy consumption. We devise a butterfly-style photonic-electronic neural chip to implement our OSNN with up to 7x fewer trainable optical components compared to GEMM-based ONNs. Additionally, a hardware-aware training framework is provided to minimize the required device programming precision, lessen the chip area, and boost the noise robustness. We experimentally demonstrate the utility of our neural chip in practical image recognition tasks, showing that a measured accuracy of 94.16% can be achieved in hand-written digit recognition tasks with 3-bit weight programming precision.Comment: 17 pages,5 figure

    ICCAD 2015 Contest in 3D Interlayer Cooling Optimized Network

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    Microchannel liquid cooling has been proposed since the late 2000s as a viable enabler for 3D integration of microprocessors to continue scaling of computing power in the face of increasingly reduced returns from CMOS technology scaling. Thermal and electrical demonstrations of microchannel liquid-cooled heat sinks on the back side of IC dies exist in the literature and the compatibility of its fabrication with the existing CMOS process has been shown. This compatibility also gives rise to the prospect of building of nearly an infinite variety of channel networks with no additional manufacturing cost. This ICCAD 2015 problem aims to identify methods to optimize such microchannel fluid networks, and to evaluate impact of different cooling networks on different computing architectures floorplans
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