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Exploring architecture, dataflow, and sparsity for gcn accelerators: A holistic framework
Recent years have seen an increasing number of Graph Convolutional Network (GCN)
models employed in various real-world applications. However, designing efficient …
models employed in various real-world applications. However, designing efficient …
Aries: Accelerating distributed training in chiplet-based systems via flexible interconnects
Large-scale deep learning models are widely deployed in many application domains with
remarkable performance improvements. However, training these models with immense …
remarkable performance improvements. However, training these models with immense …
Versa-dnn: A versatile architecture enabling high-performance and energy-efficient multi-dnn acceleration
Emerging applications utilize numerous Deep Neural Networks (DNNs) to address multiple
tasks simultaneously. As these applications continue to expand, there is a growing need for …
tasks simultaneously. As these applications continue to expand, there is a growing need for …
Aurora: A Versatile and Flexible Accelerator for Graph Neural Networks
Graph Neural Networks (GNNs) are pervasive across many application domains, driven by
the growing demands to comprehend non-euclidean data. However, it remains a challenge …
the growing demands to comprehend non-euclidean data. However, it remains a challenge …
Polyform: A versatile architecture for multi-dnn execution via spatial and temporal acceleration
Contemporary applications and cloud workloads often comprise multiple Deep Neural
Network (Multi-DNN) models. These models exhibit significant variations in computation …
Network (Multi-DNN) models. These models exhibit significant variations in computation …
[HTML][HTML] A Low-Power General Matrix Multiplication Accelerator with Sparse Weight-and-Output Stationary Dataflow
P Liu, Y Wang - Micromachines, 2025 - mdpi.com
General matrix multiplication (GEMM) in machine learning involves massive computation
and data movement, which restricts its deployment on resource-constrained devices …
and data movement, which restricts its deployment on resource-constrained devices …
Dynamic Precision-Scalable Thermal Map** Algorithm for Three Dimensional Systolic-Array Based Neural Network Accelerator
SY Lin, CK Tsai, WC Kao - IEEE Transactions on Consumer …, 2024 - ieeexplore.ieee.org
Nowadays, the systolic-array based accelerator has been used widely for the neural-
network applications. Multiple systolic-array based accelerator chips can be stacked by the …
network applications. Multiple systolic-array based accelerator chips can be stacked by the …
A dataflow architecture with distributed control for DNN acceleration
The increasing demand for edge computing requires effective Deep Neural Network (DNN)
accelerators that are suitable for resource-limited environments. This paper presents a new …
accelerators that are suitable for resource-limited environments. This paper presents a new …
Accelerating Graph Neural Network Computation on CPUs and GPUs
Q Fu - 2024 - search.proquest.com
Abstract Graph Neural Networks (GNNs) are becoming popular because of their
effectiveness in extracting structural information from graph data. Recent years have seen …
effectiveness in extracting structural information from graph data. Recent years have seen …
[PDF][PDF] FUCA: a Frame to Prevent the Generation of Useless results in the Dataflows Based on Cartesian Product for Convolutional Neural Network Accelerators
B NarimanJahan, A Khademzadeh… - International Journal of …, 2024 - journal.itrc.ac.ir
One of the most important issues in the design of CNN accelerators pertains to the
accelerator's ability to effectively leverage the available opportunities in the type and …
accelerator's ability to effectively leverage the available opportunities in the type and …