Results 11 to 20 of about 484,173 (254)
Frontiers in Neuroscience, 2022 High-quality 3D image recognition is an important component of many vision and robotics systems. However, the accurate processing of these images requires the use of compute-expensive 3D Convolutional Neural Networks (CNNs). To address this challenge, we
Gourav Datta +3 more
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Casper: Accelerating Stencil Computations Using Near-Cache Processing
IEEE Access, 2023 Stencil computations are commonly used in a wide variety of scientific applications, ranging from large-scale weather prediction to solving partial differential equations.
Alain Denzler +6 more
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Making Better Use of Processing-in-Memory Through Potential-Based Task Offloading
IEEE Access, 2020 There is an increasing demand for a novel computing structure for data-intensive applications such as artificial intelligence and virtual reality. The processing-in-memory (PIM) is a promising alternative to reduce the overhead caused by data movement ...
Byoung-Hak Kim, Chae Eun Rhee
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PIMCaffe: Functional Evaluation of a Machine Learning Framework for In-Memory Neural Processing Unit
IEEE Access, 2021 The large amount of memory usage in recent machine learning applications imposes a significant system burden with respect to power and processing speed.
Won Jeon +4 more
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Encoding processes in memory scanning tasks [PDF]
Memory & Cognition, 1976 Three experiments are presented that deal with the effect of stimulus probability on the encoding of both alphanumeric characters and nonsense figures. Experiment I replicated a previous finding of an interaction between stimulus probability and stimulus quality in a memory scanning task with numbers as stimuli.
J O, Miller, R G, Pachella
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Frontiers in Electronics, 2022 The recent advances in Artificial Intelligence (AI) achieving “better-than-human” accuracy in a variety of tasks such as image classification and the game of Go have come at the cost of exponential increase in the size of artificial neural networks. This
Vaibhav Verma, Mircea R. Stan
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A Survey of Resource Management for Processing-In-Memory and Near-Memory Processing Architectures
Journal of Low Power Electronics and Applications, 2020 Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck.
Kamil Khan +2 more
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A Survey of Near-Data Processing Architectures for Neural Networks
Machine Learning and Knowledge Extraction, 2022 Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture.
Mehdi Hassanpour +2 more
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Processing in memory: the tipping point
ETP4HPC White Paper, 2021 The tipping point for adoption of PIM is imminent for three main reasons: • Firstly, PIM avoids the von Neumann bottleneck, a fundamental limitation to the effective use of computing systems for a large range of important data-centric applications. • Secondly, it matches the community's requirement for efficient application acceleration by reducing the
Radojković, Petar +6 more
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IEEE Access, 2023 In this paper, we present a digital processing in memory (DPIM) configured as a stride edge-detection search frequency neural network (SE-SFNN) which is trained through spike location dependent plasticity (SLDP), a learning mechanism reminiscent of spike
Seong Min Kim +9 more
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