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EXTEND-L : an input language for extensible register transfer compilation
This report discusses the model and input language for EXTEND, a synthesis system that permits extensible register transfer synthesis. EXTEND-L fills the need for a language that bridges the gap between existing behavioral input descriptions, which are too abstract, and structural schematics, which cannot capture the high-level behavior. The report first discusses previous work in behavioral synthesis and summarizes the deficiencies of these behavioral specifications. The report then describes the proposed langauge in detail, and concludes with a few examples that show its utility
P4-compatible High-level Synthesis of Low Latency 100 Gb/s Streaming Packet Parsers in FPGAs
Packet parsing is a key step in SDN-aware devices. Packet parsers in SDN
networks need to be both reconfigurable and fast, to support the evolving
network protocols and the increasing multi-gigabit data rates. The combination
of packet processing languages with FPGAs seems to be the perfect match for
these requirements. In this work, we develop an open-source FPGA-based
configurable architecture for arbitrary packet parsing to be used in SDN
networks. We generate low latency and high-speed streaming packet parsers
directly from a packet processing program. Our architecture is pipelined and
entirely modeled using templated C++ classes. The pipeline layout is derived
from a parser graph that corresponds a P4 code after a series of graph
transformation rounds. The RTL code is generated from the C++ description using
Xilinx Vivado HLS and synthesized with Xilinx Vivado. Our architecture achieves
100 Gb/s data rate in a Xilinx Virtex-7 FPGA while reducing the latency by 45%
and the LUT usage by 40% compared to the state-of-the-art.Comment: Accepted for publication at the 26th ACM/SIGDA International
Symposium on Field-Programmable Gate Arrays February 25 - 27, 2018 Monterey
Marriott Hotel, Monterey, California, 7 pages, 7 figures, 1 tabl
Translating Asynchronous Games for Distributed Synthesis (Full Version)
In distributed synthesis, we generate a set of process implementations that,
together, accomplish an objective against all possible behaviors of the
environment. A lot of recent work has focussed on systems with causal memory,
i.e., sets of asynchronous processes that exchange their causal histories upon
synchronization. Decidability results for this problem have been stated either
in terms of control games, which extend Zielonka's asynchronous automata by
partitioning the actions into controllable and uncontrollable, or in terms of
Petri games, which extend Petri nets by partitioning the tokens into system and
environment players. The precise connection between these two models was so
far, however, an open question. In this paper, we provide the first formal
connection between control games and Petri games. We establish the equivalence
of the two game models based on weak bisimulations between their strategies.
For both directions, we show that a game of one type can be translated into an
equivalent game of the other type. We provide exponential upper and lower
bounds for the translations. Our translations make it possible to transfer and
combine decidability results between the two types of games. Exemplarily, we
translate decidability in acyclic communication architectures, originally
obtained for control games, to Petri games, and decidability in single-process
systems, originally obtained for Petri games, to control games
Adapting specifications for reactive controllers
For systems to respond to scenarios that were unforeseen at design time, they must be capable of safely adapting, at runtime, the assumptions they make about the environment, the goals they are expected to achieve, and the strategy that guarantees the goals are fulfilled if the assumptions hold. Such adaptation often involves the system degrading its functionality, by weakening its environment assumptions and/or the goals it aims to meet, ideally in a graceful manner. However, finding weaker assumptions that account for the unanticipated behaviour and of goals that are achievable in the new environment in a systematic and safe way remains an open challenge. In this paper, we propose a novel framework that supports assumption and, if necessary, goal degradation to allow systems to cope with runtime assumption violations. The framework, which integrates into the MORPH reference architecture, combines symbolic learning and reactive synthesis to compute implementable controllers that may be deployed safely. We describe and implement an algorithm that illustrates the working of this framework. We further demonstrate in our evaluation its effectiveness and applicability to a series of benchmarks from the literature. The results show that the algorithm successfully learns realizable specifications that accommodate previously violating environment behaviour in almost all cases. Exceptions are discussed in the evaluation
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