Always-on motion detection with application-level error control on a near-threshold approximate computing platform

Pushing supply voltages in the near-threshold region is today one of the main avenues to minimize power consumption in digital integrated circuits. This works well with logic units, but memory operations on standard six-transistor static RAM (6T-SRAM) cells become unreliable at low voltages. Standard cell memory (SCM) works fully reliably at near-threshold voltages, but has much lower area density than 6T-SRAM and thus it is too costly. Hybrid memory designs based on a combination of 6T-SRAM and SCM have the potential to combine the best from both worlds, provided that appropriate software techniques for their management are used. Several embedded applications exhibit inherent tolerance to data approximation: this feature can be exploited by mapping error-tolerant data onto unreliable 6T-SRAM while keeping critical information error-free in SCM. However, one key issue is bounding error when it is input-data dependent. In this work we consider the motion detection stage of a computer vision pipeline, which is a major power bottleneck in always-on computer vision systems. We introduce an application-level metric for defining suitable tolerance thresholds and an associated runtime mechanism for their control. At each accuracy checkpoint the error on the computation is checked. If the runtime detects that an error threshold has been exceeded, the voltage settings are adjusted. Using this methodology, we achieve a significant reduction of the total energy consumption (up to 33% in the best case) while maintaining a tight control on quality of results.