Mixed-Precision Implementation of the Density Matrix Renormalization Group.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c00632
Abstract: The mixed-precision optimization is an effective emerging technique for quantum chemistry methods to obtain better computational performance and maintain the chemical accuracy. Here, we developed a two-level mixed-precision implementation for the density matrix renormalization group… read more here.
Keywords: mixed precision; accuracy; implementation density; precision ... See more keywords
Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3007575
Abstract: Extensive convolutional neural network (CNN)-based methods have been widely used in remote sensing scene classification. However, the dense operation and huge memory storage of the state-of-the-art models hinder their deployment on low-power embedded devices. In… read more here.
Keywords: classification; quantization; method; remote sensing ... See more keywords
CMQ: Crossbar-Aware Neural Network Mixed-Precision Quantization via Differentiable Architecture Search
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2022.3197495
Abstract: The RRAM-based accelerators have become very popular candidates for neural network acceleration due to they perform matrix-vector multiplication in-memory with high storage density and low latency. Many related works have used fixed-precision quantization to achieve… read more here.
Keywords: network; mixed precision; accuracy; precision quantization ... See more keywords
Layer-Specific Optimization for Mixed Data Flow With Mixed Precision in FPGA Design for CNN-Based Object Detectors
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2020.3020569
Abstract: Convolutional neural networks (CNNs) require both intensive computation and frequent memory access, which lead to a low processing speed and large power dissipation. Although the characteristics of the different layers in a CNN are frequently… read more here.
Keywords: layer specific; mixed precision; data flow; chip ... See more keywords
MBFQuant: A Multiplier-Bitwidth-Fixed, Mixed-Precision Quantization Method for Mobile CNN-Based Applications
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2023.3268562
Abstract: Deploying Convolutional Neural Network (CNN)-based applications to mobile platforms can be challenging due to the conflict between the restricted computing capacity of mobile devices and the heavy computational overhead of running a CNN. Network quantization… read more here.
Keywords: mixed precision; quantization; cnn; precision quantization ... See more keywords
Mixed-precision weights network for field-programmable gate array
Sign Up to like & getrecommendations! Published in 2021 at "PLoS ONE"
DOI: 10.1371/journal.pone.0251329
Abstract: In this study, we introduced a mixed-precision weights network (MPWN), which is a quantization neural network that jointly utilizes three different weight spaces: binary {−1,1}, ternary {−1,0,1}, and 32-bit floating-point. We further developed the MPWN… read more here.
Keywords: precision weights; network; implementation; point ... See more keywords