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DTSTAMP:20240626T180033Z
LOCATION:3008\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240625T171500
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UID:dac_DAC 2024_sess109_RESEARCH1551@linklings.com
SUMMARY:SAS - A Framework for Symmetry-based Approximate Synthesis
DESCRIPTION:Research Manuscript\n\nNiklas Jungnitz and Oliver Keszocze (Fr
 iedrich-Alexander-Universität Erlangen-Nürnberg (FAU))\n\nApproximate Comp
 uting is a design paradigm that trades off computational accuracy for gain
 s in non-functional aspects such as reduced area, increased computation sp
 eed, or power reduction. The latter is of special interest in the field of
  Internet of Things. In this paper we present SAS, a framework for symmetr
 y-based approximate logic synthesis.  Given a Boolean multi-output functio
 n, SAS approximates it by (partially) replacing its output functions by sy
 mmetric functions with minimal Hamming distance. The framework is capable 
 of restricting the introduced error with respect to a parameterized error 
 metric that covers many real-word use-cases.\n\n        Experimental resul
 ts on common benchmark sets as well as large bit width arithmetic Boolean 
 functions confirm the effectiveness of the proposed framework.  SAS is cap
 able of synthesizing Boolean functions with size reductions of up to appro
 ximately 45% while, at the same time, respecting the specified threshold o
 n the error metric. The framework is publicly available as open-source sof
 tware on GitHub.\n\nTopic: Design\n\nKeyword: Design of Cyber-physical Sys
 tems and IoT\n\nSession Chairs: Chen Liu (Intel Corporation) and Jakub Sze
 fer (Yale University)
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