ACAM: Approximate Computing Based on Adaptive Associative Memory with Online Learning

Mohsen Imani, Yeseong Kim, Abbas Rahimi, Tajana Rosing

Abstract: The Internet of Things (IoT) dramatically increases the amount of data to be processed for many applications including multimedia. Unlike traditional computing environment, the workload of IoT significantly varies overtime. Thus, an efficient runtime profiling is required to extract highly frequent computations and pre-store them for memory-based computing. In this paper, we propose an approximate computing technique using a low-cost adaptive associative memory, named ACAM, which utilizes runtime learning and profiling. To recognize the temporal locality of data in real-world applications, our design exploits a reinforcement learning algorithm with a least recently use (LRU) strategy to select images to be profiled; the profiler is implemented using an approximate concurrent state machine. The profiling results are then stored into ACAM for computation reuse. Since the selected images represent the observed input dataset, we can avoid redundant computations thanks to high hit rates displayed in the associative memory. We evaluate ACAM on the recent AMD Southern Island GPU architecture, and the experimental results shows that the proposed design achieves by 34.7% energy saving for image processing applications with an acceptable quality of service (i.e., PSNR>30dB).