The 9th Workshop on Approximate Computing

The Approximate Computing (AxC) design paradigm emerged as a promising solution to effectively enhance performance of computing systems. It cleverly leverages the intrinsic error resilience of applications to inaccuracy in their inner calculations to achieve a required trade-off between efficiency, performance, power demand, and acceptable error of returned results. Indeed, approximate results are hardly distinguishable from exact results for a vast plethora of applications, including audio, image, and video processing, data mining, information retrieval, machine learning, artificial intelligence, and even safety-critical applications.
     
The AxC’24 workshop explores the opportunity to exploit the AxC in recent application domains in an effective, dependable, and secure manner. Moreover, new techniques are moving towards considering systems as a whole, spanning from hardware to software components, broadening the potential community of interested researchers.

This year’s event will be in conjunction with the 2024 ACM/IEEE International Conference on Computer-Aided Design (ICCAD). To learn more, please visit the ICCAD Website.

 Contributions

AxC24 accepts extended abstracts and short paper submissions, up to 4 pages plus references. Prospective attendees interested in giving a talk are invited to submit their contributions using the standard IEEE conference template through the Easy-chair submission site. All papers must be original and not simultaneously submitted to another journal or conference. 

The Organizing Committee will select the best contributions, and peer-reviewed full-paper expanded version of them will be considered for publication as part of the “Approximate Computing: the need for efficient and sustainable computing" special issue of the Elsevier Future Generation Computing System journal.

 Invited Speakers and Talks

Evolutionary approximation: From components to hardware accelerators

Vojtech Mrazek

The aim of approximation algorithms is to introduce error into the calculation in such a way that its effect on accuracy is minimal, but its effect on energy savings is as high as possible. Identifying suitable modifications can be done analytically, but it turns out that space search algorithms can correctly find them. In this keynote, algorithms based on the principles of evolution and genetic programming will be presented. These algorithms have been successfully used for both the design of basic components and for approximating relatively large computational blocks. Ways to take advantage of bioinspired algorithms and how basic scalability issues of evaluation and representation can be addressed will be presented.

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