深度神经网络(DNN)是深度学习的一种框架,它是一种具备至少一个隐层的神经网络。与浅层神经网络类似,深度神经网络也能够为复杂非线性系统提供建模,但多出的层次为模型提供了更高的抽象层次,因而提高了模型的能力。

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ISSCC(International Solid-State Circuits Conference)国际固态电路会议由IEEE固态电路协会(SSCS)举办,是世界学术界和工业界公认的集成电路设计领域最顶尖的盛会,也被认为是“芯片奥林匹克”。始于1953年的ISSCC通常是各个时期国际上最尖端固态电路技术最先发表之地。每年吸引超过3000名来自世界各地工业界和学术界的参会者。

ISSCC 在技术领域方面历经变更,ISSCC2020为“机器学习及人工智能”新成立了独立的技术小组分会,至此,ISSCC的技术分类达到12个分类,包括模拟设计(ANA)、电源管理(PM)、无线传输(WLS)、数据转换器(DC)、前瞻技术(TD)、射频技术(RF)、数字电路(DCT)、图像、 MEMS、医疗、显示(IMMD)、以及机器学习和人工智能(ML)、存储(MEM)、有线传输(WLN)和数字系统(DAS)。

来自英伟达的Rangha Venkatesan讲解了关于加速深度神经网络设计的方法教程,值得关注。

深度神经网络有着广泛的应用。与通用处理器相比,该领域的定制硬件优化提供了显著的性能和功耗优势。然而,实现高的TOPS/W和/或TOPS/mm2以及对可伸缩性和可编程性的要求是一个挑战任务。这个本教程介绍了各种设计方法,以在不同神经网络和新模型的效率、可扩展性和灵活性之间取得正确的平衡。它介绍了(i)设计高效计算单元、内存层次结构和互连拓扑的不同电路和体系结构技术,(ii)有效平铺计算的编译器方法,以及(iii)在目标硬件上高效执行的神经网络优化。

https://underline.io/lecture/13719-t7---%EF%BB%BFbasic-design-approaches-to-accelerating-deep-neural-networks

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We introduce a new attack against face verification systems based on Deep Neural Networks (DNN). The attack relies on the introduction into the network of a hidden backdoor, whose activation at test time induces a verification error allowing the attacker to impersonate any user. The new attack, named Master Key backdoor attack, operates by interfering with the training phase, so to instruct the DNN to always output a positive verification answer when the face of the attacker is presented at its input. With respect to existing attacks, the new backdoor attack offers much more flexibility, since the attacker does not need to know the identity of the victim beforehand. In this way, he can deploy a Universal Impersonation attack in an open-set framework, allowing him to impersonate any enrolled users, even those that were not yet enrolled in the system when the attack was conceived. We present a practical implementation of the attack targeting a Siamese-DNN face verification system, and show its effectiveness when the system is trained on VGGFace2 dataset and tested on LFW and YTF datasets. According to our experiments, the Master Key backdoor attack provides a high attack success rate even when the ratio of poisoned training data is as small as 0.01, thus raising a new alarm regarding the use of DNN-based face verification systems in security-critical applications.

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