25,568 research outputs found

    Using ACL2 to Verify Loop Pipelining in Behavioral Synthesis

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    Behavioral synthesis involves compiling an Electronic System-Level (ESL) design into its Register-Transfer Level (RTL) implementation. Loop pipelining is one of the most critical and complex transformations employed in behavioral synthesis. Certifying the loop pipelining algorithm is challenging because there is a huge semantic gap between the input sequential design and the output pipelined implementation making it infeasible to verify their equivalence with automated sequential equivalence checking techniques. We discuss our ongoing effort using ACL2 to certify loop pipelining transformation. The completion of the proof is work in progress. However, some of the insights developed so far may already be of value to the ACL2 community. In particular, we discuss the key invariant we formalized, which is very different from that used in most pipeline proofs. We discuss the needs for this invariant, its formalization in ACL2, and our envisioned proof using the invariant. We also discuss some trade-offs, challenges, and insights developed in course of the project.Comment: In Proceedings ACL2 2014, arXiv:1406.123

    Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

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    Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

    Characterization and Verification Environment for the RD53A Pixel Readout Chip in 65 nm CMOS

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    The RD53 collaboration is currently designing a large scale prototype pixel readout chip in 65 nm CMOS technology for the phase 2 upgrades at the HL-LHC. The RD53A chip will be available by the end of the year 2017 and will be extensively tested to confirm if the circuit and the architecture make a solid foundation for the final pixel readout chips for the experiments at the HL-LHC. A test and data acquisition system for the RD53A chip is currently under development to perform single-chip and multi-chip module measurements. In addition, the verification of the RD53A design is performed in a dedicated simulation environment. The concept and the implementation of the test and data acquisition system and the simulation environment, which are based on a modular data acquisition and system testing framework, are presented in this work
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