Mar 23, 2017 · A high-throughput accelerator for machine learning [2] achieves extremely high performance-energy ratio, 452 Giga-Operations/s at 485 mW, ...
A Super-Vector Coprocessor architecture called SVP-DL can process various matrix operations used in deep learning algorithms by calculating multidimensional ...
A Super-Vector Deep Learning Coprocessor with High Performance-Power Ratio ... ACHIEVING HIGH SPEED PERFORMANCE AND LOW POWER UTILIZATION IN NEURAL NETWORKS.
Jingfei Jiang, Zhiqiang Liu, Jinwei Xu, Rongdong Hu: A Super-Vector Deep Learning Coprocessor with High Performance-Power Ratio.
30.6: Vecim: A 289.13GOPS/W RISC-V Vector Co-Processor with Compute-in-Memory Vector Register File for Efficient High-Performance Computing. © 2024 IEEE.
This survey summarizes and classifies the most recent advances in designing DL accelerators suitable to reach the performance requirements of HPC applications.
Part I: From Machine Learning to Data Mining -- Analyzing Accident Prone Regions by Clustering -- Analyzing Life Insurance Data with Different ...
This article explains why neural network processors paired with vector DSPs are an excellent combination for a range of AI applications.
Vitruvius+: An Area-Efficient RISC-V Decoupled Vector Coprocessor for ...
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In this article, 1 we present Vitruvius+, the vector processing acceleration engine that represents the core of vector instruction execution in the HPC ...
This paper proposed an energy-efficient deep convolution neural networks coprocessor (DCNNs-CP) architecture for multi-object detection applications based ...
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