TY - JOUR A1 - Corre, Youenn A1 - Diguet, Jean-Philippe A1 - Heller, Dominique A1 - Blouin, Dominique A1 - Lagadec, Loic T1 - TBES: Template-Based Exploration and Synthesis of Heterogeneous Multiprocessor Architectures on FPGA JF - ACM transactions on embedded computing systems : TECS N2 - This article describes TBES, a software end-to-end environment for synthesizing multitask applications on FPGAs. The implementation follows a template-based approach for creating heterogeneous multiprocessor architectures. Heterogeneity stems from the use of general-purpose processors along with custom accelerators. Experimental results demonstrate substantial speedup for several classes of applications. In addition to the use of architecture templates for the overall system, a second contribution lies in using high-level synthesis for promoting exploration of hardware IPs. The domain expert, who best knows which tasks are good candidates for hardware implementation, selects parts of the initial application to be potentially synthesized as dedicated accelerators. As a consequence, the HLS general problem turns into a constrained and more tractable issue, and automation capabilities eliminate the need for tedious and error-prone manual processes during domain space exploration. The automation only takes place once the application has been broken down into concurrent tasks by the designer, who can then drive the synthesis process with a set of parameters provided by TBES to balance tradeoffs between optimization efforts and quality of results. The approach is demonstrated step by step up to FPGA implementations and executions with an MJPEG benchmark and a complex Viola-Jones face detection application. We show that TBES allows one to achieve results with up to 10 times speedup to reduce development times and to widen design space exploration. KW - Algorithms KW - Design KW - Electronic system level KW - high-level synthesis KW - multiprocessor KW - system-on-chip Y1 - 2016 U6 - https://doi.org/10.1145/2816817 SN - 1539-9087 SN - 1558-3465 VL - 15 SP - 113 EP - 122 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Heim, D. M. A1 - Heim, Olga A1 - Zeng, P. A. A1 - Zheng, Jeffrey T1 - Successful Creation of Regular Patterns in Variant Maps from Bat Echolocation Calls JF - Variant Construction from Theoretical Foundation to Applications N2 - We created variant maps based on bat echolocation call recordings and outline here the transformation process and describe the resulting visual features. The maps show regular patterns while characteristic features change when bat call recording properties change. By focusing on specific visual features, we found a set of projection parameters which allowed us to classify the variant maps into two distinct groups. These results are promising indicators that variant maps can be used as basis for new echolocation call classification algorithms. KW - Echolocation KW - Algorithms KW - Morphometry KW - Fourier KW - Analysis KW - Quaternions Y1 - 2019 SN - 978-981-13-2282-2 SN - 978-981-13-2281-5 U6 - https://doi.org/10.1007/978-981-13-2282-2_25 SP - 391 EP - 400 PB - Springer CY - Singapore ER - TY - JOUR A1 - Long, Xiang A1 - de Melo, Gerard A1 - He, Dongliang A1 - Li, Fu A1 - Chi, Zhizhen A1 - Wen, Shilei A1 - Gan, Chuang T1 - Purely attention based local feature integration for video classification JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns in videos, so as to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters (BAC), which concatenates the output of multiple attention units applied in parallel, and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling, and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order. Our final model pyramidxpyramid attention clusters (PPAC) combines both channel pyramid attention and temporal pyramid attention to focus on the most important sub-features, while also preserving the temporal information of the video. We demonstrate the effectiveness of PPAC on seven real-world video classification datasets. Our model achieves competitive results across all of these, showing that our proposed framework can consistently outperform the existing local feature integration methods across a range of different scenarios. KW - Feature extraction KW - Convolution KW - Computational modeling KW - Plugs KW - Three-dimensional displays KW - Task analysis KW - Two dimensional displays KW - Video classification KW - action recognition KW - attention mechanism KW - computer vision KW - Algorithms KW - Neural Networks KW - Computer Y1 - 2020 U6 - https://doi.org/10.1109/TPAMI.2020.3029554 SN - 0162-8828 SN - 1939-3539 SN - 2160-9292 VL - 44 IS - 4 SP - 2140 EP - 2154 PB - Inst. of Electr. and Electronics Engineers CY - Los Alamitos ER - TY - JOUR A1 - Michallek, Florian A1 - Genske, Ulrich A1 - Niehues, Stefan Markus A1 - Hamm, Bernd A1 - Jahnke, Paul T1 - Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging BT - a phantom study JF - European Radiology N2 - Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. KW - Tomography KW - X-ray computed KW - Phantoms KW - imaging KW - Liver neoplasms KW - Algorithms KW - Reproducibility of results Y1 - 2022 U6 - https://doi.org/10.1007/s00330-022-08592-y SN - 0938-7994 SN - 1432-1084 VL - 32 IS - 7 SP - 4587 EP - 4595 PB - Springer CY - New York ER -