Toward Self-Tunable Approximate Computing

Many applications show tolerance to inaccuracies. These can be exploited to build faster circuits with smaller area and lower power. This is particularly true for the hardware accelerators in heterogeneous computing systems. A major problem with approximate computing is that the resulting approximated circuit is highly dependent on the training data. Previous works often rely on static training results. If the workload is dynamic in nature or changes over time, output errors may reach unacceptable levels. Therefore, dynamic control methods are needed to solve this problem. To address this issue, in this paper, we propose an approximate self-adaptive architecture that autotunes itself at runtime based on the workload. Two different control mechanisms are proposed, one based on a regular heartbeat (HB), which resets the approximate circuits at regular intervals and the other based on internal lightweight checkers (LWCs). These checkers detect a change in the workload and reset the approximations. Experimental results show that our proposed methods work well leading to very good results compared to other approximation methods while keeping the output error within the given maximum error threshold at relatively low-area overheads, on average 8% and 8.5% for HB and LWC method, respectively, and delay overheads 5.9% and 8.1%.