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

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

Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment

MicroRNA107 (Mir107) has been thought to relate to the brain structure phenotype of Alzheimer’s disease. In this study, we evaluated the cortical anatomy in amnestic mild cognitive impairment (aMCI) and the relation between cortical anatomy and plasma levels of Mir107 and beta-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1). Twenty aMCI (20 aMCI) and 24 cognitively normal control (NC) subjects were recruited, and T1-weighted MR images were acquired. Cortical anatomical measurements, including cortical thickness (CT), surface area (SA), and local gyrification index (LGI), were assessed. Quantitative RT-PCR was used to examine plasma expression of Mir107, BACE1 mRNA. Thinner cortex was found in aMCI in areas associated with episodic memory and language, but with thicker cortex in other areas. SA decreased in aMCI in the areas associated with working memory and emotion. LGI showed a significant reduction in aMCI in the areas involved in language function. Changes in Mir107 and BACE1 messenger RNA plasma expression were correlated with changes in CT and SA. We found alterations in key left brain regions associated with memory, language, and emotion in aMCI that were significantly correlated with plasma expression of Mir107 and BACE1 mRNA. This combination study of brain anatomical alterations and gene information may shed lights on our understanding of the pathology of AD

Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development

<p>Abstract</p> <p>Background</p> <p>In eukaryotes, microRNAs (miRNAs) have emerged as critical regulators of gene expression. The Silkworm (<it>Bombyx mori </it>L.) is one of the most suitable lepidopteran insects for studying the molecular aspects of metamorphosis because of its large size, availability of mutants and genome sequence. Besides, this insect also has been amply studied from a physiological and biochemical perspective. Deep sequencing of small RNAs isolated from different stages of silkworm is a powerful tool not only for measuring the changes in miRNA profile but also for discovering novel miRNAs.</p> <p>Results</p> <p>We generated small RNA libraries from feeding larvae, spinning larvae, pupae and adults of <it>B. mori </it>and obtained ~2.5 million reads of 18-30 nt. Sequence analysis identified 14 novel and 101 conserved miRNAs. Most novel miRNAs are preferentially expressed in pupae, whereas more than 95% of the conserved miRNAs are dynamically regulated during different developmental stages. Remarkably, the miRNA-star (miR*) of four miRNAs are expressed at much higher levels than their corresponding miRNAs, and their expression profiles are distinct from their corresponding miRNA profiles during different developmental stages. Additionally, we detected two antisense miRNA loci (miR-263-S and miR-263-AS; miR-306-S and miR-306-AS) that are expressed in sense and antisense directions. Interestingly, miR-263 and miR-306 are preferentially and abundantly expressed in pupae and adults, respectively.</p> <p>Conclusions</p> <p>We identified 101 homologs of conserved miRNAs, 14 species-specific and two antisense miRNAs in the silkworm. Our results provided deeper insights into changes in conserved and novel miRNA and miRNA* accumulation during development.</p

Disrupted functional connectome in antisocial personality disorder

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD

Reduced White Matter Integrity in Antisocial Personality Disorder: A Diffusion Tensor Imaging Study

Emerging neuroimaging research suggests that antisocial personality disorder (ASPD) may be linked to abnormal brain anatomy, but little is known about possible impairments of white matter microstructure in ASPD, as well as their relationship with impulsivity or risky behaviors. In this study, we systematically investigated white matter abnormalities of ASPD using diffusion tensor imaging (DTI) measures: fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). Then, we further investigated their correlations with the scores of impulsivity or risky behaviors. ASPD patients showed decreased FA in multiple major white matter fiber bundles, which connect the fronto-parietal control network and the fronto-temporal network. We also found AD/RD deficits in some additional white matter tracts that were not detected by FA. More interestingly, several regions were found correlated with impulsivity or risky behaviors in AD and RD values, although not in FA values, including the splenium of corpus callosum, left posterior corona radiate/posterior thalamic radiate, right superior longitudinal fasciculus, and left inferior longitudinal fasciculus. These regions can be the potential biomarkers, which would be of great interest in further understanding the pathomechanism of ASPD

Reduced cortical thickness and increased surface area in antisocial personality disorder

Antisocial Personality Disorder (ASPD), one of whose characteristics is high impulsivity, is of great interest in the field of brain structure and function. However, little is known about possible impairments in the cortical anatomy in ASPD, in terms of cortical thickness and surface area, as well as their possible relationship with impulsivity. In this neuroimaging study, we first investigated the changes of cortical thickness and surface area in ASPD patients, in comparison to those of healthy controls, and then performed correlation analyses between these measures and the ability of impulse control. We found that ASPD patients showed thinner cortex while larger surface area in several specific brain regions, i.e., bilateral superior frontal gyrus, orbitofrontal and triangularis, insula cortex, precuneus, middle frontal gyrus, middle temporal gyrus, and left bank of superior temporal sulcus. In addition, we also found that the ability of impulse control was positively correlated with cortical thickness in the superior frontal gyrus, middle frontal gyrus, orbitofrontal cortex, pars triangularis, superior temporal gyrus, and insula cortex. To our knowledge, this study is the first to reveal simultaneous changes in cortical thickness and surface area in ASPD, as well as their relationship with impulsivity. These cortical structural changes may introduce uncontrolled and callous behavioral characteristic in ASPD patients, and these potential biomarkers may be very helpful in understanding the pathomechanism of ASPD

Dissecting Early Differentially Expressed Genes in a Mixture of Differentiating Embryonic Stem Cells

The differentiation of embryonic stem cells is initiated by a gradual loss of pluripotency-associated transcripts and induction of differentiation genes. Accordingly, the detection of differentially expressed genes at the early stages of differentiation could assist the identification of the causal genes that either promote or inhibit differentiation. The previous methods of identifying differentially expressed genes by comparing different cell types would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting differentially expressed genes at the early stages of differentiation. Applying the proposed method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified Smarcad1 as a novel regulator of pluripotency and self-renewal. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes