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A Survey of Near-Data Processing Architectures for Neural Networks [PDF]
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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IEEE Access, 2022 Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency and energy. A
Juan Gomez-Luna +5 more
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DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks
IEEE Access, 2021 Data movement between the CPU and main memory is a first-order obstacle against improv ing performance, scalability, and energy efficiency in modern systems.
Geraldo F. Oliveira +7 more
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Near Data Processing in Taurus Database
2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022 Huawei's cloud-native database system GaussDB for MySQL (also known as Taurus) stores data in a separate storage layer consisting of a pool of storage servers. Each server has considerable compute power making it possible to push data reduction operations (selection, projection, and aggregation) close to storage.
Shu Lin +11 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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Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research
Brain Sciences, 2021 FNIRS pre-processing and processing methodologies are very important—how a researcher chooses to process their data can change the outcome of an experiment.
Patrick W. Dans +2 more
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Practical Near-Data-Processing Architecture for Large-Scale Distributed Graph Neural Network
IEEE Access, 2022 Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification.
Linyong Huang +6 more
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Computers, 2023 The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in ...
Giacomo Donati +2 more
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Concurrent Data Structures with Near-Data-Processing [PDF]
The 31st ACM Symposium on Parallelism in Algorithms and Architectures, 2019 Recent advances in memory architectures have provoked renewed interest in near-data-processing (NDP) as way to alleviate the "memory wall" problem. An NDP architecture places logic circuits, such as simple processors, in close proximity to memory. Effective use of NDP architectures requires rethinking data structures and their algorithms.
Jiwon Choe +4 more
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Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging
Sensors, 2021 Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement.
Venkatesh Kodukula +4 more
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