In the last 10 years, the demand for new computing strategies driven by energy-efficiency has grown exponentially. Flop-per-watt (thus, per-euro) has become de-facto a driving model in hardware design. Results in this direction have been significant, leveraging first multi-core parallelism and then recently moving toward heterogeneous architectures (e.g., multicore CPU coupled with GP-GPUs). However, these evolutions will not be sufficient in the long term. To maintain an exponential increase in computational efficiency, we will need to rely either on an unlikely breakthrough discovery in hardware technology, or on a fundamental change in computing paradigms.
This workshop is dedicated to experts who explore approximation in hardware and software from both a statistical and a deterministic viewpoint, as a computing paradigm shift to break the current performance and energy-efficiency barriers of systems at all scales, from sensors to supercomputers. Approximate computing is a viable method for building more efficient, scalable and sustainable systems. However, it also places formidable challenges across the entire computing software and hardware stack. Addressing these challenges requires balanced expertise in mathematics, algorithms, software, architecture design and emerging computing platforms. The objective of this workshop is to bring together experts across these areas to present the latest findings and discuss future opportunities for approximate computing. In more detail, the workshop will cover the following areas:
- Approximate and transprecision computing: from the physical limits to the architecture and circuit design; from the algorithm design to the error analysis; from innovative technology to real applications.
- Programming abstractions: from structured and disciplined approximation in computation, communication and data transfers, to quality control and techniques to recover from over-approximation.
- Computing platforms: from tiny low-power devices for IoT applications, up to classical HPC systems embedding imprecise massively parallel accelerator.
- Applications: examples from data analytics, machine learning, deep learning, and scientific computing, where uncompromised quality with scalable order-of-magnitude time- and energy-to-solution reduction is reachable relying on approximation for a significant amount of calculations.
The workshop will cover the following key topics:
- Beyond Moore’s law
- Future challenges for programming models and languages
- Exascale Systems
The workshop provides an opportunity to have in-depth discussions, presentations, and interactions on these topics. This will promote future collaborations and better coordination around the development on approximate and transprecision computing techniques.
- Promote research and development in approximate and transprecision computing
- Align developments in algorithms, software, and hardware design towards unified and successful platforms for approximate and transprecision computing
- Foster a common discussion across multiple disciplines
- Raise energy-awareness in the big data community as well as in HPC
- Promote collaboration between academia, industry and SMEs
- Strengthen the community in energy efficient computing
The full agenda is detailed in the following table.
|START||END||Duration||THURSDAY, JUNE 25 2020|
|09:00||10:00||01:00||Invited Keynote: Frank K. Gurkaynak, ETH – Zurich, Switzerland|
|10:00||10:30||00:30||Invited Talk: Alberto Bosio, École Centrale de Lyon, France|
|10:30||11:00||00:30||Invited Talk: Dimitrios S. Nikolopoulos, Virginia Tech, USA|
|11:30||12:30||01:00||Invited Keynote: Christos-Savvas Bouganis, Imperial College, UK|
|12:30||13:00||00:30||Invited Talk: To be announced|