{'fraction_non_alphanumeric': 0.02043906131718395, 'fraction_numerical': 0.000757002271006813, 'mean_word_length': 5.849740932642487, '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}}
Efficient Resource Management of Clustered Multi-Processor Systems Through Formal Property Exploration

Modern embedded systems have adopted the clustered Chip Multi-Processor (CMP) paradigm in conjunction with dynamic frequency scaling techniques to improve application performance and power consumption. Nonetheless, modern applications are becoming more aggressive in terms of computational power. At the same time, the integration of multiple cores in the same cluster has resulted in significant increase of power consumption creating thermal hotspots. Conventional design approaches consider fixed power and temperature constraints, which are mostly extracted experimentally leading many times to pessimistic run-time decisions and performance losses. In this paper, we present a unified framework for efficient resource management of clustered CMPs by enabling formal property exploration and integrating robustness analysis. Specifically, we bridge the gap between run-time decisions and design-time exploration by using Parametric Signal Temporal Logic (PSTL) for mining the values of system constraints. Then, we utilize the extracted values to enhance the decisions of the run-time resource manager. Results on the Odroid-XU3 show that the proposed methodology offers more coarse- and fine-grain optimizations.