No abstract

No abstract

Recent researches on neural network have shown signi cant advantage in machine learning over traditional algorithms based on handcra ed features and models. Neural network is now widely adopted in regions like image, speech and video recognition. But the high computation and storage complexity of neural network inference poses great di culty on its application. CPU platforms are hard to o er enough computation capacity. GPU platforms are the rst choice for neural network process because of its high computation capacity and easy to use development frameworks.On the other hand, FPGA-based neural network inference accelerator is becoming a research topic. With speci cally designed hardware, FPGA is the next possible solution to surpass GPU in speed and energy eciency. Various FPGA-based accelerator designs have been proposed with so ware and hardware optimization techniques to achieve high speed and energy e ciency. In this paper, we give an overview of previous work on neural network inference accelerators based on FPGA and summarize the main techniques used. An investigation from so ware to hardware, from circuit level to system level is carried out to complete analysis of FPGA-based neural network inference accelerator design and serves as a guide to future work. K. Guo et al.But the computation and storage complexity of NN models are high. In Table 1, we list the number of operations, number of parameters (add or multiplication), and top-1 accuracy on ImageNet dataset [50] of state-of-the-art CNN models. Take CNN as an example. e largest CNN model for a 224 × 224 image classi cation requires up to 39 billion oating point operations (FLOP) and more than 500MB model parameters [56]. As the computation complexity is proportional to the input image size, processing images with higher resolutions may need more than 100 billion operations. Latest work like MobileNet [24] and Shu eNet [79] are trying to reduce the network size with advanced network structures, but with obvious accuracy loss. e balance between the size of NN models and accuracy is still an open question today. In some cases, the large model size hinders the application of NN, especially in power limited or latency critical scenarios. erefore, choosing a proper computation platform for neural-network-based applications is essential. A typical CPU can perform 10-100G FLOP per second, and the power e ciency is usually below 1GOP/J. So CPUs are hard to meet the high performance requirements in cloud applications nor the low power requirements in mobile applications. In contrast, GPUs o er up to 10TOP/s peak performance and are good choices for high performance neural network applications. Development frameworks like Ca e [26] and Tensor ow [4] also o er easy-to-use interfaces which makes GPU the rst choice of neural network acceleration.Besides CPUs and GPUs, FPGAs are becoming a platform candidate to achieve energy e cient neural network processing. With a neural network oriented hardware design, FPGAs can implement high parallelism and make use of the pro...

Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops rapidly and is applied to many tasks, such as image classification, face recognition, image segmentation, and human detection. Due to its superior performance, DL-based models have a wide range of application in many areas, some of which are extremely safety-critical, e.g. intelligent surveillance and autonomous driving. Due to the latency and privacy problem of cloud computing, embedded accelerators are popular in these safety-critical areas. However, the robustness of the embedded DL system might be harmed by inserting hardware/software Trojans into the accelerator and the neural network model, since the accelerator and deploy tool (or neural network model) are usually provided by third-party companies. Fortunately, inserting hardware Trojans can only achieve inflexible attack, which means that hardware Trojans can easily break down the whole system or exchange two outputs, but can't make CNN recognize unknown pictures as targets. Though inserting software Trojans has more freedom of attack, it often requires tampering input images, which is not easy for attackers. So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios. We test our attack framework for image classification and face recognition tasks, and get attack success rate of 92.6% and 100% on CIFAR10 and YouTube Faces, respectively, while keeping almost the same accuracy as the unattacked model in the normal mode. In addition, we show a specific attack scenario in which a face recognition system is attacked and gives a specific wrong answer.

Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations.