本文来源：羊城晚报，红星新闻，风暴眼，每日经济新闻 近日，拥有英国和柬埔寨双重国籍的商业巨头、"太子集团"创始人陈志，因涉嫌策划一场涉及强迫劳动的跨国"杀猪盘"加密货币骗局，被美国司法部正式起 诉。同时，美国政府宣布查获了约127271枚比特币，价值高达150亿美元（约合人民币1069亿元）。 美国司法部称，这是该部门历史上最大规模的没收行动。 柬埔寨太子集团被指控为亚洲最大跨国犯罪组织之一 美国司法部长帕姆·邦迪宣称，此次行动是"对全球人口贩卖和网络金融欺诈祸害的最重大打击之一"，并表示要"摧毁这个建立在强迫劳动和欺骗之上的犯罪 帝国"。 柬埔寨太子集团自称为一家业务遍及30多个国家的房地产、金融和消费服务公司。然而，美国司法部指控其为亚洲最大的跨国犯罪组织之一。 美国检察官披露的法庭文件显示，陈志及其团伙在柬埔寨全境建造和运营了至少十个专门用于诈骗的园区。这些园区如同监狱，内部囚禁着被虚假工作承诺 诱骗而来的劳工，并通过暴力和酷刑威胁，强迫他们对全球范围内的受害者实施诈骗。 亚洲最大跨国犯罪组织"话事人" 在柬埔寨，陈志曾是个响当当的 "人物"。38岁的他，顶着"Vincent"这个英文名，手握英国和柬埔 ...

Core Viewpoint - The article discusses the significance of Logic Optimization (LO) in chip design and introduces a novel data-driven framework called Circuit Symbolic Learning Framework (CMO) that enhances the efficiency of traditional logic optimization methods by up to 2.5 times [2][4][34]. Group 1: Introduction and Background - Chip design automation (EDA) is crucial in the semiconductor industry, with Logic Optimization being a key EDA tool aimed at improving chip quality by reducing circuit size and depth [5][7]. - Logic Optimization is an NP-hard problem, and existing heuristic algorithms face challenges due to ineffective and redundant transformations, leading to time-consuming optimization processes [8][12]. Group 2: Proposed Solution - The research team developed CMO, a data-driven framework that utilizes a teacher-student paradigm, combining Graph Neural Networks (GNN) and Monte Carlo Tree Search (MCTS) to create efficient symbolic scoring functions [6][30]. - CMO significantly improves the efficiency of key logic optimization operators, achieving a maximum speedup of 2.5 times, allowing tasks that previously took 10 minutes to be completed in just 4 minutes [4][34]. Group 3: Experimental Results - The CMO framework demonstrated substantial efficiency improvements, with traditional logic optimization operators like Mfs2 seeing their runtime reduced from 78,784 seconds to 32,001 seconds, a reduction of approximately 59.4% [34]. - The optimization quality also improved, with the maximum reduction in circuit depth reaching 30.23%, exemplified by the Hyp circuit where depth decreased from 8,259 layers to 5,762 layers, significantly lowering circuit latency [34][35].