A CNN Hardware Accelerator in FPGA for Stacked Hourglass NetworkStaked hourglass network is a widely used deep neural network model for body pose estimation. The essence of this model can be roughly considered as a combination of Deep Convolutional Neural......
a hardware Trojan is investigated that targets the reconfigurable interconnection network of CNN accelerators, resulting in incorrect computations and a decrease in inference accuracy with minimal hardware cost. The experiments demonstrated that it could lead to accuracy degradation from 8.93% to 86.20%. ...
In this work, we target a hardware/software co-design framework to accelerate the performance of CNN-based edge computing applications. The proposed framework targets FPGA technology, which offers much flexibility to update or configure the computing systems for different purposes or working conditions....
Physical computing, particularly photonic computing, offers a promising alternative by directly encoding data in physical quantities, enabling efficient probabilistic computing. This Perspective discusses the challenges and opportunities in photonic probabilistic computing and its applications in artificial intelligen...
The following pic is cropped from the HDMI output of FPGA. The white rectangle is draw by FPGA. Move a Mnist digit picture into the rectangle, or draw a digit by hand using a Paint software. The printed digit under the rectangle is the the classified result. ...
For example, a Trojan which transfers malicious code into a microprocessor's cache must do so only once if this cache is nonvolatile. Thus it is essential to consider possible resilience techniques for such security issues in the conceptualization and design phase of M-PUF or M-TRNG. It is ...
This paper proposes a hardware Trojan designed to attack a crucial component of FPGA-based CNN accelerators: the reconfigurable interconnection network. Specifically, the hardware Trojan alters the data paths during activation, resulting in incorrect connections in the arithmetic circuit and consequently ...
As the complexity of FPGA architectures increases, there is a raising need to improved productivity and performance in several computing domains such as image processing, financial analytics, edge computing and deep learning. However, vendor tools are mostly general-purpose as they attempt to provide ...
Hardware acceleratorFPGAConvolutional Neural Networks (CNNs) have been widely used in various fields due to their high accuracy and efficiency. The performance of CNNs is mainly affected by the computing capability, memory bandwidth, and flexibility of embedded devices. The high energy efficiency, ...
This article proposes a method to design and implement an FPGA-based hardware accelerator for Convolutional Neural Networks (CNNs) exploiting Partial Reconfigurability (PR). The design strategy was applied to the CloudScout CNN case study, a network developed in the frame of the \\(\\varPhi \...