{'fraction_non_alphanumeric': 0.022684310018903593, 'fraction_numerical': 0.006616257088846881, 'mean_word_length': 5.156976744186046, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An efficient memristor-based distance accelerator for time series data mining on data centers

The rapid development of Internet-of-Things (IoT) is yielding a huge volume of time series data, the real-time mining of which becomes a major load for data centers. The computation bottleneck in time series data mining is the distance function, which has been tackled by various software optimization and hardware acceleration techniques recently. However, each of these techniques is only designed or optimized for a specific distance function. To address this problem, in this paper we propose an efficient and reconfigurable memristor-based distance accelerator for real-time and energy-efficient data mining with time series on data centers. Common circuit structure is extracted to save chip areas, and the circuit can be configured to any specific distance functions. Experimental results show that compared with existing works, our work has achieved a speedup of 3.5x–376x on performance and an improvement of 1–3 orders of magnitude on energy efficiency.