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deep learning toolbox tm  (MathWorks Inc)


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    MathWorks Inc deep learning toolbox tm
    Deep Learning Toolbox Tm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 906 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/deep+learning+toolbox+tm/Deep+Learning+Toolbox/pmc12610047-235-10-15
    Average 96 stars, based on 906 article reviews
    deep learning toolbox tm - by Bioz Stars, 2026-09
    96/100 stars

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    other:

    Article Title: Temporal Neural Network Framework Adaptation in Reconfigurable Intelligent Surface-Assisted Wireless Communication
    Article Snippet: The programming is performed with MATLAB software with the help of the Deep Learning Toolbox TM .

    Article Title: Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors.
    Article Snippet: Increased availability and affordability of sensors, especially water quality sensors, is poised to improve process control and modelling in water and wastewater systems.. Sensor measurements are often flawed by unavoidable influent complexity and sensor instability, making extraction of useful signals problematic.. Although a natural reaction is to put extra effort into sensor maintenance to achieve more reliable measurements, useful signals can be extracted from those unqualified signals by appropriate usage of available data-driven tools instructed by physical factors (e.g. prior process knowledge, physical constraints, phenomenal observations).

    Article Title: Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support
    Article Snippet: To implement the ANN classifier, the MATLAB ® Deep Learning ToolBox TM was used.

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces
    Article Snippet: The NN implementation on FPGA devices is carried out using two different high-level synthesis (HLS) frameworks: the MATLAB ® Deep Learning Toolbox TM [ ] version 14.6 and the MATLAB ® Deep Learning HDL Toolbox TM [ ] version 1.5, on the one hand, and the OpenVINO toolkit [ ] along with the Intel ® FPGA AI Suite [ ], on the other hand.

    Article Title: A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection
    Article Snippet: As per the computational side, the network architectures were designed, trained, validated and tested using MATLAB ® Deep Learning Toolbox TM and a Nvidia Titan XP Graphics Processing Unit (GPU).

    Article Title: Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters
    Article Snippet: The neural network was realized by the MATLAB Deep Learning Toolbox TM.

    Article Title: Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks
    Article Snippet: The training and testing are performed in Matlab, using the Deep Learning Toolbox TM (R2021b, MathWorks, Natick, MA, USA) that provides a framework to design and implement deep neural networks, on a machine with an Intel i9-12900K CPU @ 3.20 GHz, 64 GB RAM, and an NVIDIA GeForce RTX 4090.

    Article Title: Deep learning adapted to differential neural networks used as pattern classification of electrophysiological signals
    Article Snippet: This manuscript presents the design of a deep differential neural network (DDNN) for pattern classification.. First, we proposed a DDNN topology with three layers, whose learning laws are derived from a Lyapunov analysis, justifying local asymptotic convergence of the classification error and the weights of the DDNN.. Then, an extension to include an arbitrary number of hidden layers in the DDNN is analyzed.



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    Image Search Results


    ROCK 4C Plus, NVIDIA Jetson Nano, Google Coral, and Intel ® Arria ® 10 SX SoC Development Kit specification summary.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: ROCK 4C Plus, NVIDIA Jetson Nano, Google Coral, and Intel ® Arria ® 10 SX SoC Development Kit specification summary.

    Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

    Techniques:

    NN implementation workflow for the Intel ® Arria ® 10 SX SoC Development Kit device using the MATLAB ® Deep Learning HDL Toolbox TM .

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: NN implementation workflow for the Intel ® Arria ® 10 SX SoC Development Kit device using the MATLAB ® Deep Learning HDL Toolbox TM .

    Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

    Techniques:

    Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

    Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

    Techniques:

    Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

    Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

    Techniques:

    Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

    Article Snippet: Their overall throughput is clearly superior to any of the other alternatives, with an approximately × 20 increase in performance when the Intel ® FPGA AI Suite A10_Performance architecture is compared with the MATLAB ® Deep Learning HDL Toolbox TM FP32 implementation or the NVIDIA Jetson Nano.

    Techniques:

    Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

    Article Snippet: Their overall throughput is clearly superior to any of the other alternatives, with an approximately × 20 increase in performance when the Intel ® FPGA AI Suite A10_Performance architecture is compared with the MATLAB ® Deep Learning HDL Toolbox TM FP32 implementation or the NVIDIA Jetson Nano.

    Techniques:

    Accuracy comparison of NN execution across the different devices and implementations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Accuracy comparison of NN execution across the different devices and implementations.

    Article Snippet: Their overall throughput is clearly superior to any of the other alternatives, with an approximately × 20 increase in performance when the Intel ® FPGA AI Suite A10_Performance architecture is compared with the MATLAB ® Deep Learning HDL Toolbox TM FP32 implementation or the NVIDIA Jetson Nano.

    Techniques: Comparison

    Performance comparison of NN execution across the different devices and implementations.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Performance comparison of NN execution across the different devices and implementations.

    Article Snippet: Their overall throughput is clearly superior to any of the other alternatives, with an approximately × 20 increase in performance when the Intel ® FPGA AI Suite A10_Performance architecture is compared with the MATLAB ® Deep Learning HDL Toolbox TM FP32 implementation or the NVIDIA Jetson Nano.

    Techniques: Comparison

    Graphical performance comparison for ( a ) FP32 implementations and ( b ) INT8-quantized implementations (performance of the A10_Performance implementation for the Intel ® Arria ® 10 SoC DevKit and Intel ® FPGA AI Suite is shown in green as a benchmark).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Graphical performance comparison for ( a ) FP32 implementations and ( b ) INT8-quantized implementations (performance of the A10_Performance implementation for the Intel ® Arria ® 10 SoC DevKit and Intel ® FPGA AI Suite is shown in green as a benchmark).

    Article Snippet: Their overall throughput is clearly superior to any of the other alternatives, with an approximately × 20 increase in performance when the Intel ® FPGA AI Suite A10_Performance architecture is compared with the MATLAB ® Deep Learning HDL Toolbox TM FP32 implementation or the NVIDIA Jetson Nano.

    Techniques: Comparison

    Resource usage comparison in Intel ® Arria ® 10 SX SoC Development Kit.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Resource usage comparison in Intel ® Arria ® 10 SX SoC Development Kit.

    Article Snippet: Their overall throughput is clearly superior to any of the other alternatives, with an approximately × 20 increase in performance when the Intel ® FPGA AI Suite A10_Performance architecture is compared with the MATLAB ® Deep Learning HDL Toolbox TM FP32 implementation or the NVIDIA Jetson Nano.

    Techniques: Comparison

    ROCK 4C Plus, NVIDIA Jetson Nano, Google Coral, and Intel ® Arria ® 10 SX SoC Development Kit specification summary.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: ROCK 4C Plus, NVIDIA Jetson Nano, Google Coral, and Intel ® Arria ® 10 SX SoC Development Kit specification summary.

    Article Snippet: It is also worth noting the difference in device occupation between the two MATLAB ® Deep Learning HDL Toolbox TM implementations: while the INT8-quantized version implies a slight reduction in accuracy, it is able to almost double the performance over the FP32 option thanks to a more intensive use of ALMs and, particularly, the embedded-multiplier variable-precision DSP blocks.

    Techniques:

    NN implementation workflow for the Intel ® Arria ® 10 SX SoC Development Kit device using the MATLAB ® Deep Learning HDL Toolbox TM .

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: NN implementation workflow for the Intel ® Arria ® 10 SX SoC Development Kit device using the MATLAB ® Deep Learning HDL Toolbox TM .

    Article Snippet: It is also worth noting the difference in device occupation between the two MATLAB ® Deep Learning HDL Toolbox TM implementations: while the INT8-quantized version implies a slight reduction in accuracy, it is able to almost double the performance over the FP32 option thanks to a more intensive use of ALMs and, particularly, the embedded-multiplier variable-precision DSP blocks.

    Techniques:

    Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

    Article Snippet: It is also worth noting the difference in device occupation between the two MATLAB ® Deep Learning HDL Toolbox TM implementations: while the INT8-quantized version implies a slight reduction in accuracy, it is able to almost double the performance over the FP32 option thanks to a more intensive use of ALMs and, particularly, the embedded-multiplier variable-precision DSP blocks.

    Techniques:

    Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

    doi: 10.3390/s24030899

    Figure Lengend Snippet: Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

    Article Snippet: It is also worth noting the difference in device occupation between the two MATLAB ® Deep Learning HDL Toolbox TM implementations: while the INT8-quantized version implies a slight reduction in accuracy, it is able to almost double the performance over the FP32 option thanks to a more intensive use of ALMs and, particularly, the embedded-multiplier variable-precision DSP blocks.

    Techniques: