Design Space Exploration for Memory-Oriented Approximate Computing Techniques

Exploration d'Espace de Conception pour des Techniques de Calculs Approximés pour la Mémoire

Hugo Miomandre (1, 2) , Jean Francois Nezan (1, 2) , Daniel Ménard (1, 2)

1 IETR - Institut d'Électronique et des Technologies du numéRique
2 INSA Rennes - Institut National des Sciences Appliquées - Rennes

Modern digital systems are processing more and more data. This increase in memory requirements must match the processing capabilities and interconnections to avoid the memory wall. Approximate computing techniques exist to alleviate these requirements but usually require a thorough and tedious analysis of the processing pipeline. This paper presents an applicationagnostic Design Space Exploration (DSE) of the buffer-sizing process to reduce the memory footprint of applications while guaranteeing an output quality above a defined threshold. The proposed DSE selects the appropriate bit-width and storage type for buffers to satisfy the constraint. We show in this paper that the proposed DSE reduces the memory footprint of the SqueezeNet CNN by 58.6% with identical Top-1 prediction accuracy, and the full SKA SDP pipeline by 39.7% without degradation, while only testing for a subset of the design space. The proposed DSE is fast enough to be integrated into the design stream of applications.

Mots clés: Approximate Computing; Design Space Exploration; Signal Processing; Deep Neural Network

Domaines: Informatique [cs]

Hugo Miomandre, Jean Francois Nezan, Daniel Ménard. Design Space Exploration for Memory-Oriented Approximate Computing Techniques. 33rd IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), Jul 2022, Gothenburg, Sweden. ⟨10.1109/ASAP54787.2022.00028⟩. ⟨hal-03769017⟩