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The performance of an FPGA based CNN accelerator is determined by both parallelism and frequency, however, most prior works optimize the parallelism in the RTL design and resolve the frequency after the synthesis. This paper presents a design space exploration method for the pipeline implementation of the deep CNN models, which concurrently optimizes parallelism and frequency to achieve a comprehensive...
Depth estimation from a single image is very challenging due to the inherent ambiguity of mapping a color image to a depth map. Previous work tackles this problem by exploiting various levels of features with multi-scale deep convolutional neural networks. However, most of the local geometric structure related monocular depth cues are lost when being propagated through convolutional neural network...
Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security. Arbiter PUF is a typical PUF, but is threatened by modeling attacks. To resist attack, XOR arbiter PUF employs multiple basic arbiter PUFs and XOR their response bits to generate the final response bit. However, its low reliability not only limits its applications, but also leaks information to enhance...
Physical Unclonable Function {PUF) is severely threatened by modeling attacks. This paper proposes a novel Polymorphic PUF for CPU+FPGA SoC. We fully exploit the dynamic reconfigurability of the SoC to minimize the Challenge Response Pair (CRP) correlation so as to resist modeling attacks. An asymmetric RO pair is proposed to produce the response. Experiments on real CPU+FPGA SoCs show the high resistance...
The security and stable operation of thermal power units plays a very important role in a power system, so the reliability evaluation of thermal power units has become a critical problem. By combining the advantages of the Analytic Hierarchy Process (AHP) and the Elimination et Choice Translating Reality (ELECTRE) III, a reliability evaluation method for thermal power units based on AHP and ELECTRE-III...
The lack of observability of prototype chips makes post silicon debug extremely difficult and time consuming. Trace based debug techniques can improve the observability by acquiring some internal states at runtime through a dedicated on-chip trace buffer. In this paper, we propose a flip-flop clustering based trace signal selection method, which uses the forward tracing to generate flip-flop clusters...
In hydraulic piping system, health management has become a challenging task. As operating in harsh environment, damage detection and fault diagnosis are important to hydraulic pipes. In this paper, an intelligent monitoring system is proposed for hydraulic pipes based on multiple sensors. Firstly, it collects and records various parameters of pipes such as vibration, hydraulic pressure, temperature,...
Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security. If faults happen in PUF during manufacturing, the security of whole chip will be threatened. Fault diagnosis plays an important role in the yield learning process. However, since different manufactured PUFs with the same design have different Challenge-Response Pairs (CRPs), which cannot be predicted,...
Convolutional Neural Networks (CNN) are verycomputation-intensive. Recently, a lot of CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we observed that there is a big mismatch between the parallel types supported by computing engine and the dominant parallel types of CNN workloads. This mismatch seriously degrades resource utilization of existing accelerators. In this...
Kinematics is the basis of robotic control, which manages the robots' movement, walking and balancing. As a critical part of Kinematics, the Inverse Kinematics (IK) will consume more time and energy to figure out the solution with the degrees of freedom increase. It goes beyond the ability of general-purpose processor based methods to provide real-time IK solver for manipulators with high degree of...
Due to the recent progress in deep learning and neural acceleration architectures, specialized deep neural network or convolutional neural network (CNNs) accelerators are expected to provide an energy-efficient solution for real-time vision/speech processing, recognition and a wide spectrum of approximate computing applications. In addition to their wide applicability scope, we also found that the...
Aiming at real-life problems, microrobotic systems have gained more and more attention. However, limited achievable performance of microrobotic system prevents it from carrying out complex tasks. Current research work propose customize designs for different applications and incorporate dedicated accelerator for high energy efficiency. However, not only such techniques require significant manual effort...
Multi-granularity memory system provides multiple access granularities for the applications with various spatial localities. In the multi-granularity access pattern, the one-size-bandwidth NoC design cannot utilize the bandwidth efficiently. We propose a novel NoC design, called BoDNoC, which can merge multiple narrow subnets to provide various bandwidths for access data. The new design also adopts...
Processing-in-Memory (PIM), has recently been revisited as one of the most promising solutions to deal with the issue of bandwidth and power wall between processor and memory. In this paper, we propose a light-weight PIM architecture, approxPIM, which leverages approximate computing techniques to enable InMemory Processing in a realistic 3D-stacked DRAM, Micron's Hybrid Memory Cube (HMC). Using the...
This paper surveys how to use Convolutional Neural Networks (CNN) to hypothesize object location and categorization from images or videos in mobile heterogeneous SoCs. Recently a variety of CNN-based object detection frameworks have demonstrated both increasing accuracy and speed. Though they are making fast progress in high quality image recognition, state-of-the-art CNN-based detection frameworks...
The Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security. The strong PUFs with numerous Challenge-Response Pairs (CRPs), such as various arbiter PUFs, mirror current PUF, and voltage transfer PUF, are severely threatened by the machine learning based modeling attacks. To handle this issue, we propose the Physical Unclonable Function with Randomized challenge...
Depression is a common mental disorder, and in recent years, there has been an increasing trend of mild to moderate depression among college students. Additionally, effective detection of mild depression at an earlier stage remains an urgent problem that must be solved. In this study, electroencephalography (EEG) activities were recorded from 37 participants during processing of facial expression...
Physical Unclonable Function (PUF) is a new hardware security primitive that exploits the manufacturing variations of integrated circuits. Traditional arbiter PUF is vulnerable to machine learning based modeling attacks due to its linearity. Current mirror PUF uses non-linear current mirror to bring non-linearity into the challenge-response relationship and is claimed resistant to modeling attacks...
Trustworthiness of a hardware design has caused great concerns, including the malicious modifications on the design made by Electrical Design Automation (EDA) tools of third-party. In this paper, a novel hardware Trojan (HT) detection method based on the property coverage analysis is proposed to verify the synthesized netlist. The proposed method is motivated by the observation that the malicious...
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