Optical Network-on-Chip (ONoC) is a promising communication architecture which supports multiple communication tasks communicating at the same time at different wavelengths, i.e., Wavelength Division Multiplexing (WDM...
详细信息
Edge computing is a continuum that includes the computing resources from cloud to things. Ecosystem of things (EoT) is a subsystem of the ecosystem of edge computing, which potentially contains trillions of devices of...
详细信息
LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that capt...
详细信息
Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in mo...
详细信息
Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient...
详细信息
Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay op-timization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is ob-tained using a novel Reed-Muller expression simplification approach (RMESA) considering don't-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark cir-cuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most cir-cuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.
Conventional reconfigurable architectures, e.g., Field-Programmable Gate Array (FPGA), are confronted with the inflexibility of the on-chip local memory architecture and the scarce memory resource, which result in the...
详细信息
Conventional reconfigurable architectures, e.g., Field-Programmable Gate Array (FPGA), are confronted with the inflexibility of the on-chip local memory architecture and the scarce memory resource, which result in the inflexibility to deal with the current memory-thirsty data-intensive applications. Emerging nonvolatile devices, such as resistive random-access memory (RRAM), can operate in a logic-in-memory way, i.e., they can act as both a nonvolatile memory element and a switch, and thus, have the potential to provide higher flexibility and data processing ability. We propose a field-programmable logic-in-memory architecture based on RRAM technology, which is a two-dimensional tile array, where each tile is an RRAM-based crossbar array and can be remolded into three basic modes including logic, interconnect and memory. We further develop a specific adaptive algorithm for placement and routing, which can well exploit the logic-in-memory architecture characteristics. Comparing with traditional FPGAs, our architecture shows 1.9x lower power consumption, 2.8x lower delay, and 5.6x higher performance.
Injecting faults to the systemarchitecture layer and studying the upper neural network for fault tolerancehe is difficult and time-consuming. This paper proposes an automatic method covering time and space, which can...
ISBN:
(数字)9781728143903
ISBN:
(纸本)9781728143910
Injecting faults to the systemarchitecture layer and studying the upper neural network for fault tolerancehe is difficult and time-consuming. This paper proposes an automatic method covering time and space, which can inject faults into the processor on the Simics simulation platform, simulating soft errors, and then collect the time sequence data of the systemarchitecture layer and the observed node data of visual convolutional neural networks program layer. At the same time, combined with the relevant standards, the GAN classifier is used to calibrate the different fault models after converting time sequence data into time sequence images. Finally, the Bayesian network is used to form the path of fault propagation from the architecture layer to the program layer and the result layer. After intensive fault injection into critical registers, the probability of neural network failure caused by soft errors is effectively stimulated.
Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of dia...
详细信息
Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit
Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for...
详细信息
SpMV is an essential kernel existing in many HPC and data center applications. Meanwhile, the emerging many-core hardware provides promising computational power, and is widely used for acceleration. Many methods and f...
详细信息
暂无评论