Asynchronous Dataflow De-Elastisation for Efficient Heterogeneous Synthesis

Mamaghani, MJ; Sokolov, D; Garside, J

doi:10.1109/ACSD.2016.22

Asynchronous Dataflow De-Elastisation for Efficient Heterogeneous Synthesis

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Abstract

© 2016 IEEE. Algorithmic synthesis provides flexibility in design space exploration and improves design productivity by separating the concerns of system timing and functionality. This enables a designer to cope with the rapid increase of SoC complexity and to employ different computation and communication models with various timing constraints. De-elastisation emerged as a technique that transforms timing-free concurrent dataflow models to synchronous circuits while offering selective timing flexibility in the design. We adopt De-elastisation in an in-house EDA flow: it starts from a system specification in the Balsa language and uses eTeak to generate an elastic network of macro-modules. Based on structural analysis of the obtained network some of its portions are selectively transformed into synchronous circuits' in a supervised fashion' targeting better power and performance in the computation domain' whilst preserving fine-grained elasticity between communicating modules to handle timing uncertainties. We evaluate De-elastisation and compare it against some popular high-level synthesis technologies' namely LegUp' Bluespec' Chisel and Balsa using a set of benchmarks from the domain of Database Management Systems (DBMS) accelerators. Our experiments demonstrate the efficacy of Dataflow Decomposition and De-elastisation on the selected range of applications and its advantages in exploring the design trade-offs: a twofold increase in performance and 15% decrease in power consumption can be achievable at the expense of moderate area overhead.