{'fraction_non_alphanumeric': 0.035416666666666666, 'fraction_numerical': 0.00625, 'mean_word_length': 5.281045751633987, '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}}
Generating Brain-Network-Inspired Topologies for Large-Scale NoCs on Monolithic 3D ICs

Network-on-chips (NoCs) with traditional regular topologies generally have an average-hop-count increased, polynomially, with the network size. This brief proposes to generate brain-network-inspired (BNI) topologies on monolithic 3D (M3D) ICs, where the average hop count scales up logarithmically, for large-scale systems with tens of thousands of cores. We propose a routability-driven method to generate BNI topologies on M3D ICs with scale-free and small-world properties. Moreover, we present an iterative method to reduce the network diameter using as few tier-crossing routers as possible. The resulting BNI topologies not only have over 50% lower average hop count and lower diameter than traditional regular topologies, but also have shorter total link length than other irregular topologies. The performance is further validated by the cycle-accurate simulation.