Demand Layering for Real-Time DNN Inference with Minimized Memory Usage

When executing a deep neural network (DNN), its model parameters are loaded into GPU memory before execution, incurring a significant GPU memory burden. There are studies that reduce GPU memory usage by exploiting CPU memory as a swap device. However, this approach is not applicable in most embedded systems with integrated GPUs where CPU and GPU share a common memory. In this regard, we present Demand Layering, which employs a fast solid-state drive (SSD) as a co-running partner of a GPU and exploits the layer-by-layer execution of DNNs. In our approach, a DNN is loaded and executed in a layer-by-layer manner, minimizing the memory usage to the order of a single layer. Also, we developed a pipeline architecture that hides most additional delays caused by the interleaved parameter loadings alongside layer executions. Our implementation shows a 96.5% memory reduction with just 14.8% delay overhead on average for representative DNNs. Furthermore, by exploiting the memory-delay tradeoff, near-zero delay overhead (under 1 ms) can be achieved with a slightly increased memory usage (still an 88.4% reduction), showing the great potential of Demand Layering.Comment: 14 pages, 16 figures. Accepted to the 43rd IEEE Real-Time Systems Symposium (RTSS), 202

Factors controlling the distribution of dissolved organic carbon and nitrogen in the coastal waters off Jeju Island

The composition of dissolved organic matter (DOM) in the coastal waters off Jeju Island, Korea, originates from a complex mixture of organic sources. This study examined the dynamics and sources of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in the coastal waters off Jeju Island. Seasonal variation in the DOC and DON concentrations was observed, with significantly higher levels during summer (DOC: 82 ± 15 µM and DON: 6.8 ± 2.0 µM) than during the other seasons. In 2017, the Kuroshio Intermediate Water had a greater impact on the coastal waters off Jeju Island during winter (79%) and spring (69%) than during the other seasons, while the Changjiang Diluted Water (CDW) (12%) and the Kuroshio Surface Water (47%) had a stronger impact during summer and the Yellow Sea Cold Water (10%) had a stronger impact during autumn. Although water mass analysis provides valuable insights, certain aspects of the DOM distribution in coastal seawater remain unexplained. During summer, while the mixing of the CDW influenced the concentrations of DOC and DON, a distinct pulse in these concentrations was observed within a specific salinity range, suggesting microbial activity as a source. The relationship between dissolved inorganic nitrogen (DIN) and salinity also exhibited the opposite trend to that between DON and salinity, indicating the conversion of DON into DIN through microbial activity. These findings suggest that microbial activity plays a key role in the observed DOM pulse, transforming particulate organic matter into DOM and then converting it into DIN during the long transportation from Changjiang River to Jeju Island. This organic matter cycle could thus serve as a source of DIN in oligotrophic regions. However, further research on the sources and distribution of organic matter using biogeochemical parameters is required to gain a better understanding of the intricate processes involved

Ex vivo Dynamics of Human Glioblastoma Cells in a Microvasculature-on-a-Chip System Correlates with Tumor Heterogeneity and Subtypes

The perivascular niche (PVN) plays an essential role in brain tumor stem-like cell (BTSC) fate control, tumor invasion, and therapeutic resistance. Here, a microvasculature-on-a-chip system as a PVN model is used to evaluate the ex vivo dynamics of BTSCs from ten glioblastoma patients. BTSCs are found to preferentially localize in the perivascular zone, where they exhibit either the lowest motility, as in quiescent cells, or the highest motility, as in the invasive phenotype, with migration over long distance. These results indicate that PVN is a niche for BTSCs, while the microvascular tracks may serve as a path for tumor cell migration. The degree of colocalization between tumor cells and microvessels varies significantly across patients. To validate these results, single-cell transcriptome sequencing (10 patients and 21 750 single cells in total) is performed to identify tumor cell subtypes. The colocalization coefficient is found to positively correlate with proneural (stem-like) or mesenchymal (invasive) but not classical (proliferative) tumor cells. Furthermore, a gene signature profile including PDGFRA correlates strongly with the “homing” of tumor cells to the PVN. These findings demonstrate that the model can recapitulate in vivo tumor cell dynamics and heterogeneity, representing a new route to study patient-specific tumor cell functions

Impaired Inflammatory Responses in Murine Lrrk2-Knockdown Brain Microglia

LRRK2, a Parkinson's disease associated gene, is highly expressed in microglia in addition to neurons; however, its function in microglia has not been evaluated. Using Lrrk2 knockdown (Lrrk2-KD) murine microglia prepared by lentiviral-mediated transfer of Lrrk2-specific small inhibitory hairpin RNA (shRNA), we found that Lrrk2 deficiency attenuated lipopolysaccharide (LPS)-induced mRNA and/or protein expression of inducible nitric oxide synthase, TNF-α, IL-1β and IL-6. LPS-induced phosphorylation of p38 mitogen-activated protein kinase and stimulation of NF-κB-responsive luciferase reporter activity was also decreased in Lrrk2-KD cells. Interestingly, the decrease in NF-κB transcriptional activity measured by luciferase assays appeared to reflect increased binding of the inhibitory NF-κB homodimer, p50/p50, to DNA. In LPS-responsive HEK293T cells, overexpression of the human LRRK2 pathologic, kinase-active mutant G2019S increased basal and LPS-induced levels of phosphorylated p38 and JNK, whereas wild-type and other pathologic (R1441C and G2385R) or artificial kinase-dead (D1994A) LRRK2 mutants either enhanced or did not change basal and LPS-induced p38 and JNK phosphorylation levels. However, wild-type LRRK2 and all LRRK2 mutant variants equally enhanced NF-κB transcriptional activity. Taken together, these results suggest that LRRK2 is a positive regulator of inflammation in murine microglia, and LRRK2 mutations may alter the microenvironment of the brain to favor neuroinflammation

Attenuation of ultrasonic shear waves in copper

The attenuation of circularly polarized shear waves propagating in the [001] direction in copper was calculated for external magnetic fields up to 32 kG. The attenuation by electrons on different regions of the Fermi surface was identified. This study used the Fermi surface calculated from the studies of M. R. Haise by L. T. Wood.Physics, Department o

오디오 캡셔닝

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. Gunhee Kim.본 논문은, 오디오 캡셔닝이라는 주제를 최초로 다룬다. 자연에서 발생하는 소리 를 인간의 언어로 표현하는 문제는 아직 아무도 다뤄보지 않은 중요한 문제이다. 새로운 문제를 이해하고 풀기위해선 학습에 있어 가장 중요한 데이터가 필요하 다. 우리는 약 9만3천개 가량의 자연의 소리에 관한 해설을 크라우드 소싱하여 AudioCaps라는 새로운 데이터셋을 구성하였다. 본 논문에서는 철저한 실험들을 통하여 AudioCaps 데이터셋의 높은 품질을 증명하고, 오미오 캡셔닝이라는 새 로운 문제에 어떤 input representation이 적합한지 꼼꼼하게 확인을 해보았다. 추가로, 자연에서 발생하는 오디오의 특징을 발견하여, 2가지의 새로운 테크닉을 선보인다. 첫번째는, Top-Down multi-scale encoder이라는 방법을 통해 오디오의 세부적인 내용까지 학습하는데 고려를 하도록하고, 두번째로는 aligned semantic attention을 통해 오디오와 주어지는 semantic cue가 정렬이 잘 맞을 수 있도록 유도한다. 이 두가지 테크닉을 이용하여, 새로운 state-of-the-art 성능을 갱신해보 인다We explore the problem of audio captioning: generating natural language descriptions for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 93K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Our thorough empirical studies not only show that our collected captions are indeed faithful to audio inputs but also discover what forms of audio representation and captioning models are effective for the audio captioning. From extensive experiments, we also propose two novel components that help improve audio captioning performance: the top-down multi-scale encoder and aligned semantic attention.Abstract i Contents ii List of Figures iv List of Tables vi Chapter 1 Introduction 1 Chapter 2 Related Works 4 Chapter 3 The Audio Captioning Dataset 7 3.0.1 AudioSet Tailoring . . . . . . . . . . . . . . . . . . . . . . 7 3.0.2 Audio Annotation . . . . . . . . . . . . . . . . . . . . . . 9 3.0.3 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . 11 3.0.4 Dataset Comparison . . . . . . . . . . . . . . . . . . . . . 11 Chapter 4 Approach 18 4.0.1 Top-down Multi-scale Encoder . . . . . . . . . . . . . . . 19 4.0.2 Aligned Semantic Attention . . . . . . . . . . . . . . . . . 20 Chapter 5 Experiments 23 5.0.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . 23 5.0.2 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.0.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Chapter 6 Conclusion 34 Chapter 7 Appendix 42 요약 45 Acknowledgements 46Maste