Energy efficiency optimization of FPGA-based CNN accelerators with full data reuse and VFS

Abstract—While FPGA has been recognized as a promising platform to accelerate Convolutional Neural Networks (CNNs) in embedded computing given its high flexibility and power efficiency, two challenges still have to be addressed to enhance its applicability on the edgecomputing paradigm. First, the power and performance of the CNN accelerator are still bounded by memory throughput, and a CNNcustomized architecture is desirable to fully utilize the on-chip storage. Second, power optimization algorithms are insufficiently explored on CNN-targeted platforms. In this paper, we design a novel FPGA-based CNN accelerator architecture that makes full use of the on-chip storage resources leveraging data reuse and loop unrolling strategies. We also present an efficient FPGA-based voltage and frequency scaling (VFS) system that enables VFS of the CNN accelerator for power optimization. We devise a VFS policy that fully exploits the power efficiency potential of the FPGA. Experiment results show up to 40% energy can be saved with our VFS platform and policy

Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction.

MRI is an advanced imaging modality with the unfortunate disadvantage of long data acquisition time. To accelerate MR image acquisition while maintaining high image quality, extensive investigations have been conducted on image reconstruction of sparsely sampled MRI. Recently, deep convolutional neural networks have achieved promising results, yet the local receptive field in convolution neural network raises concerns regarding signal synthesis and artifact compensation. In this study, we proposed a deep learning-based reconstruction framework to provide improved image fidelity for accelerated MRI. We integrated the self-attention mechanism, which captured long-range dependencies across image regions, into a volumetric hierarchical deep residual convolutional neural network. Basically, a self-attention module was integrated to every convolutional layer, where signal at a position was calculated as a weighted sum of the features at all positions. Furthermore, relatively dense shortcut connections were employed, and data consistency was enforced. The proposed network, referred to as SAT-Net, was applied on cartilage MRI acquired using an ultrashort TE sequence and retrospectively undersampled in a pseudo-random Cartesian pattern. The network was trained using 336 three dimensional images (each containing 32 slices) and tested with 24 images that yielded improved outcome. The framework is generic and can be extended to various applications

Tetraethyl 2,2′-(2,3,5,6-tetrafluoro-p-phenylenedimethylene)dipropanoate1

In the mol­ecule of the title compound, C22H26F4O8, a crystallographic inversion centre is located at the centroid of the benzene ring. C—H⋯F and C—H⋯O intra­molecular hydrogen bonds are observed as well as an inter­molecular C—H⋯O inter­action

Quantitative MRI Musculoskeletal Techniques: An Update.

OBJECTIVE. For many years, MRI of the musculoskeletal system has relied mostly on conventional sequences with qualitative analysis. More recently, using quantitative MRI applications to complement qualitative imaging has gained increasing interest in the MRI community, providing more detailed physiologic or anatomic information. CONCLUSION. In this article, we review the current state of quantitative MRI, technical and software advances, and the most relevant clinical and research musculoskeletal applications of quantitative MRI

Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling

On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy

Expression of miR-126 and its potential function in coronary artery disease

Objective: This study aimed to explore the role of miR-126 in coronary artery disease (CAD) patients and the potential gene targets of miR-126 in atherosclerosis.Methodology: A total of 60 CAD patients and 25 healthy control subjects were recruited in this study. Among the 60 CAD patients, 18 cases were diagnosed of stable angina pectoris (SAP), 20 were diagnosed of unstable angina pectoris (UAP) and 22 were diagnosed of acute myocardial infarction (AMI). Plasma miR-126 levels from both groups of participants were analyzed by real-time quantitative PCR. ELISA was used to measure plasma level of placenta growth factor (PLGF).Results: The results showed that the miR-126 expression was significantly down-regulated in the circulation of CAD patients compared with control subjects (P<0.01). Plasma PLGF level was significantly upregulated in patients with unstable angina pectoris and acute myocardial infarction (AMI) compared with controls (both P<0.01) the miR-126 expression in AMI was significantly associated with PLGF.Conclusion: miR-126 may serve as a novel biomarker for CAD.Keywords: miR-126; PLGF; PCR; coronary artery disease; atherosclerosi

Rapid identification and prognosis evaluation by dual-phase computed tomography angiography for stroke patients with a large ischemic region in the anterior circulation treated with endovascular thrombectomy

PurposeTo investigate the value of dual-phase head-and-neck computed tomography angiography (CTA) in assessing advantages and risks associated with mechanical thrombectomy for stroke with a large ischemic region in the anterior circulation within 6 h of onset.MethodsWe retrospectively analyzed the data of patients with acute occlusion of the internal carotid artery or middle cerebral artery-M1 segment. Baseline dual-phase CTA was performed for collateral grading using the 4-point visual collateral score (0, 0% filling; 1, >0% and ≤50% filling; 2, >50 and <100% filling; 3, 100% filling). The rates of modified Rankin score (MRS) ≤ 3 at 90 days, any intracranial hemorrhage (ICH) within 48 h, malignant cerebral edema within 24 h, and all-cause 90-day mortality were analyzed.ResultsAmong the 69 study patients, 15, 26, 17, and 11 patients had collateral grades of 0, 1, 2, and 3, respectively. At 90 days, the MRS was ≤3 in 0, 8.33, 29.41, and 36.36% of patients with grades 0, 1, 2, and 3, respectively. ICH incidence was 73.33, 57.69, 29.41, and 18.18% for grades 0, 1, 2, and 3, respectively, while the incidence of malignant brain edema was 100, 76.92, 35.29, and 0%, respectively. All-cause 90-day mortality was 53.33% for grade 0 and 30.77% for grade 1; no deaths occurred at grades 2 and 3.ConclusionCollateral grading based on dual-phase CTA enables simple and rapid preoperative evaluation prior to mechanical thrombectomy for acute anterior-circulation stroke with a large ischemic focus, particularly for patients presenting within the 6-h time window