In digital hardware, numbers are stored in binary words. A binary word is a fixed-length sequence of bits (1's and 0's). The data type defines how hardware components or software functions interpret this sequence of 1's and 0's. Numbers are represented as either scaled integer (usually referred to as fixed-point) or floating-point data types.
Most pretrained neural networks and neural networks trained using Deep Learning Toolbox™ use single-precision floating point data types. Even small trained neural networks require a considerable amount of memory, and require hardware that can perform floating-point arithmetic. These restrictions can inhibit deployment of deep learning capabilities to low-power microcontrollers and FPGAs.
Using the Deep Learning Toolbox Model Quantization Library support package, you can quantize a network to use 8-bit scaled integer data types.
Quantization of a neural network requires a GPU, the GPU Coder™ Interface for Deep Learning Libraries support package, and the Deep Learning Toolbox Model Quantization Library support package. Using a GPU requires a CUDA® enabled NVIDIA® GPU with compute capability 6.1, 6.3 or higher.
Scaled 8-bit integer data types have limited precision and range when compared to single-precision floating point data types. There are several numerical considerations when casting a number from a larger floating-point data type to a smaller data type of fixed length.
Precision loss: Precision loss is a rounding error. When precision loss occurs, the value is rounded to the nearest number that is representable by the data type. In the case of a tie it rounds:
Positive numbers to the closest representable value in the direction of positive infinity.
Negative numbers to the closest representable value in the direction of negative infinity.
In MATLAB® you can perform this type of rounding using the round
function.
Underflow: Underflow is a type of precision loss. Underflows occur when the value is smaller than the smallest value representable by the data type. When this occurs, the value saturates to zero.
Overflow: When a value is larger than the largest value that a data type can represent, an overflow occurs. When an overflow occurs, the value saturates to the largest value representable by the data type.
Use the Deep Network Quantizer app to collect and visualize the dynamic ranges of the weights and biases of the convolution layers and fully connected layers of a network, and the activations of all layers in the network. The app assigns a scaled 8-bit integer data type for the weights, biases, and activations of the convolution layers of the network. The app displays a histogram of the dynamic range for each of these parameters. The following steps describe how these histograms are produced.
For example, to begin, consider the following values logged for a parameter while exercising a network.
Find the ideal binary representation of each logged value of the parameter.
The most significant bit (MSB) is the left-most bit of the binary word. This bit contributes most to the value of the number. The MSB for each value is highlighted in yellow.
By aligning the binary words, you can see the distribution of bits used by the logged values of a parameter. Sum the number of MSB's in each column for an aggregate view of the logged values.
Display the MSB counts of each bit location as a heat map. In this heat map, darker blue regions correspond to a larger number of MSB's in the bit location.
The software assigns a data type that can represent the bit locations that capture the most information. In this example, the software selects a data type that represents bits from 23 to 2-3. An additional sign bit is required to represent the signedness of the value.
After assigning the data type, any bits outside of that data type are removed. In this sample, the first value, 0.03125, suffers from an underflow, so the quantized value is 0. The ideal value 2.1 suffers some precision loss, so the quantized value is 2.125. The value 16.250 is larger than the largest representable value of the data type, so this value overflows. The quantized value saturates to 15.874.
The app displays this heat map histogram for each learnable parameter in the convolution layers and fully connected layers of the network. The gray regions of the histogram show the bits that cannot be represented by the data type.
Matlabsolutions.com provides guaranteed satisfaction with a
commitment to complete the work within time. Combined with our meticulous work ethics and extensive domain
experience, We are the ideal partner for all your homework/assignment needs. We pledge to provide 24*7 support
to dissolve all your academic doubts. We are composed of 300+ esteemed Matlab and other experts who have been
empanelled after extensive research and quality check.
Matlabsolutions.com provides undivided attention to each Matlab
assignment order with a methodical approach to solution. Our network span is not restricted to US, UK and Australia rather extends to countries like Singapore, Canada and UAE. Our Matlab assignment help services
include Image Processing Assignments, Electrical Engineering Assignments, Matlab homework help, Matlab Research Paper help, Matlab Simulink help. Get your work
done at the best price in industry.
Desktop Basics - MATLAB & Simulink
Array Indexing - MATLAB & Simulink
Workspace Variables - MATLAB & Simulink
Text and Characters - MATLAB & Simulink
Calling Functions - MATLAB & Simulink
2-D and 3-D Plots - MATLAB & Simulink
Programming and Scripts - MATLAB & Simulink
Help and Documentation - MATLAB & Simulink
Creating, Concatenating, and Expanding Matrices - MATLAB & Simulink
Removing Rows or Columns from a Matrix
Reshaping and Rearranging Arrays
Add Title and Axis Labels to Chart
Change Color Scheme Using a Colormap
How Surface Plot Data Relates to a Colormap
How Image Data Relates to a Colormap
Time-Domain Response Data and Plots
Time-Domain Responses of Discrete-Time Model
Time-Domain Responses of MIMO Model
Time-Domain Responses of Multiple Models
Introduction: PID Controller Design
Introduction: Root Locus Controller Design
Introduction: Frequency Domain Methods for Controller Design
DC Motor Speed: PID Controller Design
DC Motor Position: PID Controller Design
Cruise Control: PID Controller Design
Suspension: Root Locus Controller Design
Aircraft Pitch: Root Locus Controller Design
Inverted Pendulum: Root Locus Controller Design
Get Started with Deep Network Designer
Create Simple Image Classification Network Using Deep Network Designer
Build Networks with Deep Network Designer
Classify Image Using GoogLeNet
Classify Webcam Images Using Deep Learning
Transfer Learning with Deep Network Designer
Train Deep Learning Network to Classify New Images
Deep Learning Processor Customization and IP Generation
Prototype Deep Learning Networks on FPGA
Deep Learning Processor Architecture
Deep Learning INT8 Quantization
Quantization of Deep Neural Networks
Custom Processor Configuration Workflow
Estimate Performance of Deep Learning Network by Using Custom Processor Configuration
Preprocess Images for Deep Learning
Preprocess Volumes for Deep Learning
Transfer Learning Using AlexNet
Time Series Forecasting Using Deep Learning
Create Simple Sequence Classification Network Using Deep Network Designer
Train Classification Models in Classification Learner App
Train Regression Models in Regression Learner App
Explore the Random Number Generation UI
Logistic regression create generalized linear regression model - MATLAB fitglm 2
Support Vector Machines for Binary Classification
Support Vector Machines for Binary Classification 2
Support Vector Machines for Binary Classification 3
Support Vector Machines for Binary Classification 4
Support Vector Machines for Binary Classification 5
Assess Neural Network Classifier Performance
Discriminant Analysis Classification
Train Generalized Additive Model for Binary Classification
Train Generalized Additive Model for Binary Classification 2
Classification Using Nearest Neighbors
Classification Using Nearest Neighbors 2
Classification Using Nearest Neighbors 3
Classification Using Nearest Neighbors 4
Classification Using Nearest Neighbors 5
Gaussian Process Regression Models
Gaussian Process Regression Models 2
Understanding Support Vector Machine Regression
Extract Voices from Music Signal
Align Signals with Different Start Times
Find a Signal in a Measurement
Extract Features of a Clock Signal
Filtering Data With Signal Processing Toolbox Software
Find Periodicity Using Frequency Analysis
Find and Track Ridges Using Reassigned Spectrogram
Classify ECG Signals Using Long Short-Term Memory Networks
Waveform Segmentation Using Deep Learning
Label Signal Attributes, Regions of Interest, and Points
Introduction to Streaming Signal Processing in MATLAB
Filter Frames of a Noisy Sine Wave Signal in MATLAB
Filter Frames of a Noisy Sine Wave Signal in Simulink
Lowpass Filter Design in MATLAB
Tunable Lowpass Filtering of Noisy Input in Simulink
Signal Processing Acceleration Through Code Generation
Signal Visualization and Measurements in MATLAB
Estimate the Power Spectrum in MATLAB
Design of Decimators and Interpolators
Multirate Filtering in MATLAB and Simulink