Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection,traffic classification,and adaptive Quality-of-Service...Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection,traffic classification,and adaptive Quality-of-Service(QoS)control.This paper proposes NeuroPulse,a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks(SNNs)with the representational power of sparse self-attention,enabling efficient deployment on neuromorphic network processors(NNPs).We propose a Rate-Coded Cross-Attention(RCCA)module,which converts population-coded spike-trains into attention queries,allowing long-range dependency modeling within sub-milliwatt(sub-mW)power budgets.NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a Hebbian Synaptic Consolidation(HSC)mechanism,eliminating the need for full model retraining.Experiments on NSL-KDD,UNSW-NB15,and real-world 5G RAN telemetry datasets demonstrate that NeuroPulse achieves 94.3%intrusion detection accuracy at 0.23 mW average energy consumption—a 12×power reduction over transformer-only baselines—while retaining 97.1%of accumulated knowledge after 50 sequential task updates,making it uniquely suited for always-on intelligent network nodes.展开更多
A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuch...A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.展开更多
目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频...目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频率调节(Dynamic Voltage Frequency Scaling,DVFS)技术,研究异构多核实时系统中基于任务同步的节能调度问题,提出同步感知的最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First,SA-LESF)。该算法针对所有任务的速度配置进行迭代优化,直至所有任务均达到其最大限度节能的速度配置。此外,进一步提出基于动态松弛时间回收的同步感知最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First with Dynamic Reclamation,SA-LESF-DR)。该算法在保证实时任务可调度的同时,实施相应的回收策略,进一步降低系统能耗。实验结果表明,SA-LESF与SA-LESF-DR算法在能耗表现上具有优势,在相同任务集下,相比其他算法可节省高达30%的能耗。展开更多
模糊测试通过生成多样化测试用例发现语言处理器中的潜在缺陷,但现有基于大语言模型(large language model,LLM)的方法在异常程序生成能力及测试用例有效性方面仍存在不足。针对上述问题,提出一种基于历史缺陷信息的通用模糊测试用例生...模糊测试通过生成多样化测试用例发现语言处理器中的潜在缺陷,但现有基于大语言模型(large language model,LLM)的方法在异常程序生成能力及测试用例有效性方面仍存在不足。针对上述问题,提出一种基于历史缺陷信息的通用模糊测试用例生成方法HisFuzz,并实现原型工具。该方法利用多语言处理器的历史缺陷数据,通过向量化检索与定向突变策略引导LLM生成具有异常特征的测试用例,并结合语法修复与有效性检查机制,提高测试用例的可编译性与执行成功率。在测试阶段,采用差分测试技术分析语言处理器的行为差异。实验结果表明,在C、C++、Go和Python语言处理器的评估中,HisFuzz在测试用例有效性和代码覆盖率方面均优于现有方法,平均有效性提升20.33%,覆盖率提升32.56%,并成功发现6个设计缺陷。实验表明,HisFuzz能够有效提升基于LLM的模糊测试性能。展开更多
摘要Conventional deep learning networks impose prohibitive energy requirements on continuously operational network intelligence applications such as anomaly detection,traffic classification,and adaptive Quality-of-Service(QoS)control.This paper proposes NeuroPulse,a spiking-transformer hybrid neural architecture that combines the temporal sparsity of spiking neural networks(SNNs)with the representational power of sparse self-attention,enabling efficient deployment on neuromorphic network processors(NNPs).We propose a Rate-Coded Cross-Attention(RCCA)module,which converts population-coded spike-trains into attention queries,allowing long-range dependency modeling within sub-milliwatt(sub-mW)power budgets.NeuroPulse also supports catastrophe-free continual learning on non-stationary network traffic distributions via a Hebbian Synaptic Consolidation(HSC)mechanism,eliminating the need for full model retraining.Experiments on NSL-KDD,UNSW-NB15,and real-world 5G RAN telemetry datasets demonstrate that NeuroPulse achieves 94.3%intrusion detection accuracy at 0.23 mW average energy consumption—a 12×power reduction over transformer-only baselines—while retaining 97.1%of accumulated knowledge after 50 sequential task updates,making it uniquely suited for always-on intelligent network nodes.
摘要A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.
摘要目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频率调节(Dynamic Voltage Frequency Scaling,DVFS)技术,研究异构多核实时系统中基于任务同步的节能调度问题,提出同步感知的最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First,SA-LESF)。该算法针对所有任务的速度配置进行迭代优化,直至所有任务均达到其最大限度节能的速度配置。此外,进一步提出基于动态松弛时间回收的同步感知最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First with Dynamic Reclamation,SA-LESF-DR)。该算法在保证实时任务可调度的同时,实施相应的回收策略,进一步降低系统能耗。实验结果表明,SA-LESF与SA-LESF-DR算法在能耗表现上具有优势,在相同任务集下,相比其他算法可节省高达30%的能耗。
摘要模糊测试通过生成多样化测试用例发现语言处理器中的潜在缺陷,但现有基于大语言模型(large language model,LLM)的方法在异常程序生成能力及测试用例有效性方面仍存在不足。针对上述问题,提出一种基于历史缺陷信息的通用模糊测试用例生成方法HisFuzz,并实现原型工具。该方法利用多语言处理器的历史缺陷数据,通过向量化检索与定向突变策略引导LLM生成具有异常特征的测试用例,并结合语法修复与有效性检查机制,提高测试用例的可编译性与执行成功率。在测试阶段,采用差分测试技术分析语言处理器的行为差异。实验结果表明,在C、C++、Go和Python语言处理器的评估中,HisFuzz在测试用例有效性和代码覆盖率方面均优于现有方法,平均有效性提升20.33%,覆盖率提升32.56%,并成功发现6个设计缺陷。实验表明,HisFuzz能够有效提升基于LLM的模糊测试性能。