Design Space Exploration for Memory-Oriented Approximate Computing Techniques

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 application-agnostic 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.