Doppelganger: A Cache For Approximate Computing

Modern processors contain large last level caches (LLCs) that consume substantial energy and area yet are imperative for high performance. Cache designs have improved dramatically by considering reference locality. Data values are also a source of optimization. Compression and deduplication exploit data values to use cache storage more efficiently resulting in smaller caches without sacrificing performance. In multi-megabyte LLCs, many identical or similar values may be cached across multiple blocks simultaneously. This redundancy effectively wastes cache capacity. We observe that a large fraction of cache values exhibit approximate similarity. More specifically, values across cache blocks are not identical but are similar. Coupled with approximate computing which observes that some applications can tolerate error or inexactness, we leverage approximate similarity to design a novel LLC architecture: the Doppelganger cache. The Doppelganger cache associates the tags of multiple similar blocks with a single data array entry to reduce the amount of data stored. Our design achieves 1.55x, 2.55x and 1.41x reductions in LLC area, dynamic energy and leakage energy without harming performance nor incurring high application error.