Enabling Approximate Storage through Lossy Media Data Compression

As compute capabilities continue to scale, memory capacity and bandwidth continue to lag behind. Data compression is an effective approach to improving memory capacity and bandwidth; but prior works have focused primarily on lossless compression and retrieval for data-critical applications. While data integrity is critical to many applications, a growing number of memory-bound applications contain non-critical program data; this property can be exploited to improve memory performance and capacity. In this work, we enable approximate storage to exploit the inherent error resilience of multimedia and other applications, through lossy compression, to improve storage without adversely affecting application functionality. We realize an incidence of approximate storage through modification of the Base-Delta-Immediate algorithm to create a lossy memory compression algorithm. We conclude that approximate storage is an effective and promising technique for improving effective memory capacity and decreasing the energy cost of memory access; results show that memory capacity can be increased by 15% with only 3% loss in image recognition application quality.