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: Intel ® Arria ® 10 SoC DevKit & MATLAB ® DL Toolbox TM , 51.19 , 19.54 , 104.92 , 9.53.

Techniques:

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: Intel ® Arria ® 10 SX SoC Development Kit test setup. The dataflow related to the NN accelerator creation and deployment is represented in blue, while the test dataflow is illustrated in black.

Article Snippet: Intel ® Arria ® 10 SoC DevKit & MATLAB ® DL Toolbox TM , 51.19 , 19.54 , 104.92 , 9.53.

Techniques:

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: Intel ® Arria ® 10 SoC DevKit & MATLAB ® DL Toolbox TM , 51.19 , 19.54 , 104.92 , 9.53.

Techniques:

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: Intel ® Arria ® 10 SoC DevKit & MATLAB ® DL Toolbox TM , 51.19 , 19.54 , 104.92 , 9.53.

Techniques: Comparison

Journal: Ieee Access

Article Title: Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey

doi: 10.1109/ACCESS.2021.3054484

Figure Lengend Snippet: Articles Proposing Novel Methods for COVID-19 Detection via CXR Images

Article Snippet: , , Multi-level segmentation thresholding was applied to split grayscale images into areas of intensities to reduce the number of objects in a lung image. An SVM was then used to classify the COVID-19 cases. The MATLAB 2019a DL toolbox was used..

Techniques: Labeling, Extraction, Biomarker Discovery

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: MATLAB ® DL Toolbox TM FP32 , Used BM % , 134,187 195.54% , 23,133,724 113.11% , 2131 193.38% , 255 42.08%.

Techniques:

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: MATLAB ® DL Toolbox TM FP32 , Used BM % , 134,187 195.54% , 23,133,724 113.11% , 2131 193.38% , 255 42.08%.

Techniques:

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: MATLAB ® DL Toolbox TM FP32 , Used BM % , 134,187 195.54% , 23,133,724 113.11% , 2131 193.38% , 255 42.08%.

Techniques: Comparison

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: MATLAB ® DL Toolbox TM FP32 , Used BM % , 134,187 195.54% , 23,133,724 113.11% , 2131 193.38% , 255 42.08%.

Techniques: Comparison

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: MATLAB ® DL Toolbox TM FP32 , Used BM % , 134,187 195.54% , 23,133,724 113.11% , 2131 193.38% , 255 42.08%.

Techniques: Comparison