1,034 research outputs found
Kosciusko [music] /
No full textFor voice and piano.; Cover title.; "Introduced & sung by Miss Nella Webb."; Cover carries portraits of Nella Webb (by Rudolph Buchner), Charles Vaude and Moritz Lutzen.; Words printed as text on p. [4].; "During Moritz Lutzen's visit to Australia he offered a prize for the best lyric, by an Australian author to be set to music by himself. The prize was awarded to Charles Vaude, for his lyric 'Kosciusko,' and Miss Nella Webb produced this song with instantaneous success."--P. [4].; Also available online http://nla.gov.au/nla.mus-an8393500; 1913, by Victor J. Draper, Sydney.; NLA's NL copy from the collection of Keith Watson. ANL
xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems
No full textISSN:2160-051
J/inference end-to-end gesture recognition from dynamic vision sensor data using ternarized hybrid convolutional neural networks
Full text linkDynamic vision sensor (DVS) cameras enable energy-activity proportional visual sensing by only propagating events produced by changes in the observed scene. Furthermore, by generating these events asynchronously, they offer
s-scale latency while eliminating the redundant data transmission inherent to classical, frame-based cameras. However, the potential of DVS to improve the energy efficiency of IoT sensor nodes can only be fully realized with efficient and flexible systems that tightly integrate sensing, processing, and actuation capabilities. In this paper, we propose a complete end-to-end pipeline for DVS event data classification implemented on the Kraken parallel ultra-low power (PULP) system-on-chip and apply it to gesture recognition. A dedicated on-chip peripheral interface for DVS cameras aggregates the received events into ternary event frames. We process these video frames with a fully ternarized two-stage temporal convolutional network (TCN). The neural network can be executed either on Kraken’s PULP cluster of general-purpose RISC-V cores or on CUTIE, the on-chip ternary neural network accelerator. We perform extensive ablations on network structure, training, and data generation parameters. We achieve a validation accuracy of 97.7 % on the DVS128 11-class gesture dataset, a new record for embedded implementations. With in-silicon power and energy measurements, we demonstrate a classification energy of 7
J at a latency of 0.9 ms when running the TCN on CUTIE, a reduction of inference energy by
when compared to the state of the art in embedded gesture recognition. The processing system consumes as little as 4.7 mW in continuous inference, enabling always-on gesture recognition and closing the gap between the efficiency potential of DVS cameras and application scenarios
Securing Tiny Transformer-based Computer Vision Models: Evaluating Real-World Patch Attacks
Full text linkTransformers have significantly impacted the field of Computer Vision (CV) and the Internet of Things (IoT), sur-passing Convolutional Neural Networks (CNN) in various tasks. However, ensuring the security of CV models for critical real-world IoT applications such as autonomous driving, surveillance, and biomedical technologies is crucial. The adversarial robustness of these models has become a key research area, especially for edge processing. This work evaluates the robustness of Swin tiny and ConvNeXt tiny, specifically focusing on real-world patch attacks in Object Detection scenarios. To ensure a fair comparison, we establish a level playing field between Transformer based and CNN architectures, examining their vulnerabilities and potential defenses. Experimental results demonstrate the susceptibility of the Swin tiny and ConvNeXt tiny models to
patch attacks, resulting in a significant decrease in average precision (AP) for the ”Person” class. When trained adversarial patches were applied, the AP drops to 12.8% and 15.2% for Swin tiny and ConvNeXt tiny models, respectively, highlighting their vulnerability to these attacks. This paper contributes to securing CV models on IoT vision devices, providing insights into the robustness of transformer-based architectures against real-world attacks, and advancing the field of adversarial robustness in embedded computer vision
CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency
Full text linkWe present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks (TNNs). CUTIE, the completely unrolled ternary inference engine, focuses on minimizing noncomputational energy and switching activity so that dynamic power spent on storing (locally or globally) intermediate results is minimized. This is achieved by: 1) a data-path architecture completely unrolled in the feature map and filter dimensions to reduce switching activity by favoring silencing over iterative computation and maximizing data reuse; 2) targeting TNNs which, in contrast to binary NNs, allow for sparse weights that reduce switching activity; and 3) introducing an optimized training method for higher sparsity of the filter weights, resulting in a further reduction of the switching activity. Compared with state-of-the-art accelerators, CUTIE achieves greater or equal accuracy while decreasing the overall core inference energy cost by a factor of 4.8x-21x
WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for Distributed Surveillance
No full textThe trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling medium and long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter
Letter containing inquiry regarding the ethnic identity of the descendents of Georg Moritz Oppenheim.
No full textLetter from Wilhelm Gehlig to Rabbi Dr. Freudenthal in Nuremberg with a genealogical question regarding Georg Moritz Oppenheim. Of particular interest to the author is to determine whether Oppenheim's descendents are "rein jüdischen Blutes (=of pure Jewish blood)."Robert Singermandigitize
Conventional and circular economy compliant modification strategies for recycled polypropylene
No full textAuthor Moritz MagerMasterarbeit Universität Linz 2021Arbeit gesperr
Conventional and circular economy compliant modification strategies for recycled polypropylene
No full textAuthor Moritz MagerMasterarbeit Universität Linz 2021Arbeit gesperr
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
Full text linkTransformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and audio processing. However, the efficient hardware acceleration of transformer models poses new challenges due to their high arithmetic intensities, large memory requirements, and complex dataflow dependencies. In this work, we propose ITA, a novel accelerator architecture for transformers and related models that targets efficient inference on embedded systems by exploiting 8-bit quantization and an innovative softmax implementation that operates exclusively on integer values. By computing on-the-fly in streaming mode, our softmax implementation minimizes data movement and energy consumption. ITA achieves competitive energy efficiency with respect to state-of-the-art transformer accelerators with 16.9 TOPS/W, while outperforming them in area efficiency with 5.93 TOPS/mm in 22 nm fully-depleted silicon-on-insulator technology at 0.8 V.Accepted for publication at the 2023 ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED
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