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日前,广和通携手中国移动、中国电信、中国联通、高通公司、紫光展锐、联发科技以及众多物联网产业伙伴正式发布“5G智造营”年度创新成果,并重磅推出《5G AIoT全景商用产品手册》,以生态力量打破行业边界,以技术融合“智造”商业价值。

本产品手册汇集了5G AIOT领域产业生态合作的主要案例,其中包括电信运营商、芯片商、智慧能源、工业互联、固定无线接入、C-V2X、智慧零售、智联万物、物联网生态九大领域。

5G作为数字经济核心产业,在未来5-10年中对于经济发展将产生深远影响。从2019年6月工信部正式向四家运营商发放5G商用牌照至今2年多时间,在全社会和产业界的共同努力下,我国5G商用稳步推进,5G产业生态持续壮大,应用创新日益活跃,目前在基础设施、产业能力、融合应用等各个方面形成全球领先优势。根据工信部相关数据显示,截止今年6月底,我国累计建设5G基站达96.1万个,覆盖全国所有地级以上城市,5G手机终端连接数达3.65亿户,占全球80%以上;中国电信和中国联通已建成全球规模最大的5G共建共享网络,累计节约投资超过860亿元。

5G更广阔的市场是面向千行百业的各类智能物联网(AIOT)应用场景,为国民经济各行业数字化转型提供有力支撑。经过2年多的实践,5G在我国各行各业的技术创新和深度应用已有明显成效,5G+工业互联网、超高清视频、智慧教育、智慧医疗、健康养老等典型应用加快发展,全国5G应用创新案例超过1万个,多个行业的5G应用已经从“样板间”开始走向“商品房”阶段。

今年7月,工信部、中央网信办、国家发改委等十部委联合发布《5G应用“扬帆”行动计划(2021-2023年)》,作为接下来3年中5G产业发展的“总纲领”,明确了2021年5G应用发展的主要指标,并确定了15个重点应用领域的行动方案。值得关注的是,该行动计划提出了“推动5G模组规模化商用”的目标,包括构建建模组分级分类产业化体系,指导行业面向差异化场景需求开展精准化产品研发,持续提升模组的环境适应性,不断降低规模化应用门槛。

众所周知,物联网模组是实现各垂直行业终端快速智能化连接的核心中间件,在产业数字化中发挥关键作用,因此模组规模化也是5G应用规模化发展的必要条件。4G时代,中国企业在全球蜂窝物联网模组市场中逐渐占据主导地位;5G时代,预计中国企业不仅将继续巩固在这一领域的主导地位,还将形成创新引领。

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To reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties, which is going to be more prevalent in futuristic artificial intelligence of things (AIoT) systems. These AIoT systems anticipate horizontal collaboration among different resource-constrained AIoT node devices, where CNN layers are partitioned and these devices collaboratively compute complex CNN tasks. This horizontal collaboration opens another attack surface to the CNN-based application, like inserting the hardware Trojans (HT) into the embedded accelerators designed for the CNN. Therefore, there is a dire need to explore this attack surface for designing secure embedded hardware accelerators for CNNs. Towards this goal, in this paper, we exploited this attack surface to propose an HT-based attack called FeSHI. Since in horizontal collaboration of RC AIoT devices different sections of CNN architectures are outsourced to different untrusted third parties, the attacker may not know the input image, but it has access to the layer-by-layer output feature maps information for the assigned sections of the CNN architecture. This attack exploits the statistical distribution, i.e., Gaussian distribution, of the layer-by-layer feature maps of the CNN to design two triggers for stealthy HT with a very low probability of triggering. Also, three different novel, stealthy and effective trigger designs are proposed.

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