25. – 30. Juli 2021, Dagstuhl-Seminar 21302

Approximate Systems


Eva Darulova (MPI-SWS – Kaiserslautern, DE)
Babak Falsafi (EPFL – Lausanne, CH)
Andreas Gerstlauer (University of Texas at Austin, US)
Phillip Stanley-Marbell (University of Cambridge, GB)

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Resource efficiency is becoming an increasingly important challenge, especially due to the pervasiveness of computing systems and the diminishing returns from performance improvements of process technology scaling. At the same time, many important applications have nondeterministic specifications or are robust to noise in their execution. They thus do not necessarily require fully reliable computing systems and their resource consumption can be reduced by introducing or exposing approximations.

While trading correctness for efficiency has been part of computing systems since the early days, it has seen renewed interest in the past decade. Different techniques have been since developed for applying and controlling approximations and the errors they introduce at different levels of the compute stack. Unfortunately, most of these techniques have been applied in isolation, making simplified assumptions about the other levels. It is thus unclear how all the different techniques interact, combine and complement or negate each other to provide end-to-end application benefits.

The aim of this seminar was to bring together researchers from different domains working on approximate computing, algorithms, programming languages, compilers, architecture and circuits, in order to explore open challenges and opportunities and to define cross-area research directions and collaborations relating to an end-to-end application of approximate computing principles across the compute stack.

The seminar consisted of brief presentations by a subset of the participants that covered the entire computing stack from hardware to applications, and that focused on the current challenges. The talks were followed by discussions in breakout groups that first focused on the different application areas of high-performance computing, embedded systems and deep learning, followed by group discussions on particular fundamental and cross-cutting challenges that were identified during the first breakout session. This report includes the abstracts of the participant's presentations as well as summaries of the breakout group discussions.

Summary text license
  Creative Commons BY 4.0
  Eva Darulova, Babak Falsafi, Andreas Gerstlauer, and Phillip Stanley-Marbell


  • Hardware
  • Programming Languages / Compiler


  • Approximate computing
  • Energy-efficient computing
  • Pareto optimization


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Download Übersichtsflyer (PDF).

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