Results 161 to 170 of about 35,691 (191)
Seeing a sunset: Exploring the joy of vision, in healthy eyes and ocular disease. [PDF]
Ophthalmic Physiol OptAnderson AJ +3 more
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Longitudinal graphics of patient-reported physical function in patients treated for hematologic malignancies. [PDF]
BMC Med Res MethodolThanarajasingam G +15 more
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Extreme nonlinearity by layered materials through inverse design. [PDF]
Sci AdvZhao Z, Kundu RD, Sigmund O, Zhang XS.
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Deep learning applications for diabetic retinopathy and retinopathy of prematurity diseases diagnosis: a systematic review. [PDF]
Int J OphthalmolMutua EN, Kasamani BS, Reich C.
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Platelets regulate neural and oligodendroglial progenitors when infiltrating the brain parenchyma. [PDF]
Commun BiolDimitriou C +20 more
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Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data ...
Amit Livne +3 more
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Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data ...
Amit Livne +3 more
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Proceedings of the 2021 Great Lakes Symposium on VLSI, 2021
Analysis of Physically Unclocnable Functions (PUFs) from a Boolean function perspective, and the efficient hardware implementation of such Boolean representations, can potentially lead to interesting insights about their behavior and robustness. Such a circuit implementation can also be a convenient substitute for the machine learning model of a PUF ...
Pranesh Santikellur +2 more
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Analysis of Physically Unclocnable Functions (PUFs) from a Boolean function perspective, and the efficient hardware implementation of such Boolean representations, can potentially lead to interesting insights about their behavior and robustness. Such a circuit implementation can also be a convenient substitute for the machine learning model of a PUF ...
Pranesh Santikellur +2 more
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Proceedings of the 56th Annual Design Automation Conference 2019, 2019
Binary STT-MRAM is a highly anticipated embedded non-volatile memory technology in advanced logic nodes < 28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STT-MRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning ...
Chih-Cheng Chang +10 more
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Binary STT-MRAM is a highly anticipated embedded non-volatile memory technology in advanced logic nodes < 28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STT-MRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning ...
Chih-Cheng Chang +10 more
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LP-BNN: Ultra-low-Latency BNN Inference with Layer Parallelism
2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019High inference latency seriously limits the deployment of DNNs in real-time domains such as autonomous driving, robotic control, and many others. To address this emerging challenge, researchers have proposed approximate DNNs with reduced precision, e.g., Binarized Neural Networks (BNNs).
Tong Geng +6 more
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Physically Tightly Coupled, Logically Loosely Coupled, Near-Memory BNN Accelerator (PTLL-BNN)
ESSCIRC 2019 - IEEE 45th European Solid State Circuits Conference (ESSCIRC), 2019In this paper, a physically tightly coupled, logically loosely coupled, near-memory binary neural network accelerator (PTLL-BNN) is designed and fabricated. Both architecture-level and circuit-level optimizations are presented. From the perspective of processor architecture, the PTLL-BNN includes two new design choices.
Yun-Chen Lo +7 more
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