Flexible Bit-Truncation Memory for Low-Power Quality-Adaptive Video and Deep Learning Storage

Bit-truncation has demonstrated great potential to enable run-time quality-power adaptive data storage and thus enhance the power efficiency of data-intensive applications such as videos and deep learning. However, existing bit-truncation memories are custom designed for a specific working condition of the target application. In this paper, we present a novel bit-truncation memory with full truncation flexibility, and it can truncate any number of bits for optimal tradeoff between quality requirements of applications and power savings. Our experiments show that the proposed memory can support three different video applications (including luminance-aware, content-aware, and region-of-interest-aware) with enhanced power efficiency (up to 50.03% power savings) as compared to state-of-the art solutions. Also, the proposed memory achieves up to 66.56% and 63.29% power savings for baseline and lightweight deep learning models respectively, with a low implementation cost (2.42%).