Snapdragon Neural Processing Engine SDK
Reference Guide

The default output of snpecaffetodlc and tools_snpecaffe2todlc is a nonquantized model. This means that all the network parameters are left in the 32 floating point representation as present in the original Caffe model. To quantize the model to 8 bit fixed point, see snpedlcquantize. Note that models that are intended to be quantized using snpedlcquantize must have their batch dimension set to 1. A different batch dimension can be used during inference, by resizing the network during initialization.
The default output of snpeonnxtodlc is a nonquantized model. This means that all the network parameters are left in the 32 bit floating point representation as present in the original ONNX model. To quantize the model to 8 bit fixed point, see snpedlcquantize. Note that models that are intended to be quantized using snpedlcquantize must have their batch dimension set to 1. A different batch dimension can be used during inference, by resizing the network during initialization.
The default output of snpetensorflowtodlc is a nonquantized model. This means that all the network parameters are left in the 32 bit floating point representation as present in the original TensorFlow model. To quantize the model to 8 bit fixed point, see snpedlcquantize. Note that models that are intended to be quantized using snpedlcquantize must have their batch dimension set to 1. A different batch dimension can be used during inference, by resizing the network during initialization.
Runtime  Quantized DLC  NonQuantized DLC 

CPU or GPU  Compatible. The model is dequantized by the runtime, increasing network initialization time. Accuracy may be impacted.  Compatible. The model is native format for these runtimes. Model can be passed directly to the runtime. May be more accurate than a quantized model. 
DSP  Compatible. The model is native format for DSP runtime. Model can be passed directly to the runtime. Accuracy may be different than a nonquantized model  Compatible. The model is quantized by the runtime, increasing network initialization time. Accuracy may be different than a quantized model. 
AIP  Compatible. The model is in supported format for AIP runtime. Model can be passed directly to the runtime.  Incompatible. Nonquantized models are not supported by the AIP runtime. 
This section describes the concepts behind the quantization algorithm used in SNPE. These concepts are used by snpedlcquantize and is also used by SNPE for input quantization when using the DSP runtime.
Note: SNPE supports multiple quantization modes. The basics of the quantization, regardless of mode, are described here. See Quantization Modes for more information.
SNPE currently supports a default quantization bit width of 8 for both weights and biases. The bias bit width, however, can be overriden to use 32 bit quantization by specifying the command line option "bias_bitwidth 32" from snpedlcquantize. For some models, using 32bit biases may give a small improvement in accuracy. Unfortunately it is difficult to predict which models may benefit from this since model architectures, weight distributions, etc all have an impact on quantization performance.
SNPE supports multiple quantization modes, the difference is in how quantization parameters are chosen.
The default mode has been described above, and uses the true min/max of the data being quantized, followed by an adjustment of the range to ensure a minimum range and to ensure 0.0 is exactly quantizable.
Enhanced quantization mode (invoked by using the "use_enhanced_quantizer" parameter to snpedlcquantize) uses an algorithm to try to determine a better set of quantization parameters to improve accuracy. The algorithm may pick a different min/max value than the default quantizer, and in some cases it may set the range such that some of the original weights and/or activations cannot fall into that range. However, this range does produce better accuracy than simply using the true min/max. The enhanced quantizer can be enabled independently for weights and activations by appending either "weights" or "activations" after the option.
This is useful for some models where the weights and/or activations may have "long tails". (Imagine a range with most values between 100 and 1000, but a few values much greater than 1000 or much less than 100.) In some cases these long tails can be ignored and the range 100, 1000 can be used more effectively than the full range.
Enhanced quantizer still enforces a minimum range and ensures 0.0 is exactly quantizable.
This mode is used only for quantizing weights to 8 bit fixed point(invoked by using the "use_adjusted_weights_quantizer" parameter to snpedlcquantize), which uses adjusted min or max of the data being quantized other than true min/max or the min/max that exclude the long tail. This has been verified to be able to provide accuracy benefit for denoise model specifically. Using this quantizer, the max will be expanded or the min will be decreased if necessary.
Adjusted weights quantizer still enforces a minimum range and ensures 0.0 is exactly quantizable.
Quantization can be a difficult problem to solve due to the myriad of training techniques, model architectures, and layer types. In an attempt to mitigate quantization problems two new model preprocessing techniques have been added to snpedlcquantize that may improve quantization performance on models which exhibit sharp drops in accuracy upon quantization.
The two new techniques introduced are CLE (Cross Layer Equalization) and BC (Bias Correction).
CLE works by scaling the convolution weight ranges in the network by making use of a scaleequivariance property of activation functions. In addition, the process absorbs high biases which may be result from weight scaling from one convolution layer to a subsequent convolution layer.
BC corrects the biased error that is introduced in the activations during quantization. It does this by accumulating convolution/MatMul activation data from the floatingpoint model and the quantized model and then corrects for the statistical bias on the layer’s output. This correction is added to the bias of the layer in question.
In many cases CLE+BC may enable quantized models to return to close to their original floatingpoint accuracy. There are some caveats/limitations to the current algorithms:
Typically CLE and BC are best when used together as they complement one another. CLE is very fast (seconds), however BC can be extremely slow (minutes to hours) as quantization data must be run through the network repeatedly as corrections are made. When time is critical it might be prudent to try CLE first and check the accuracy of the resulting model. If the accuracy is good (within a couple % of the floatingpoint model) then CLE+BC should be run on the original model and will likely give better results. In cases where the accuracy is still poor BC is unlikely to provide much additional accuracy gain.
To run the typical use case, CLE+BC, pass the "optimizations cle optimizations bc" to snpedlcquantize.
To run only CLE pass the "optimizations cle" to snpedlcquantize.
The original converted float model should always be used as input to snpedlcquantize. Passing quantized models back to the quantizer is not supported and will result in undefined behavior. In addition, BC should not be run on its own without CLE as it won’t fix major issues with weight/activation quantization.
More information about the algorithms can be found here: https://arxiv.org/abs/1906.04721
Quantizing a model and/or running it in a quantized runtime (like the DSP) can affect accuracy. Some models may not work well when quantized, and may yield incorrect results. The metrics for measuring impact of quantization on a model that does classification are typically "Mean Average Precision", "Top1 Error" and "Top5 Error". These metrics published in SNPE release notes for various models.
If the option –quantization_overrides is provided during model conversion the user can provide a json file with parameters to use for quantization. These will be cached along with the model and can be used to override any quantization data carried from conversion (eg TF fake quantization) or calculated during the normal quantization process in snpedlcquantize. To override the params during snpedlcquantize the option –override_params must be passed, and the cached values will be used instead. The json format is defined as per AIMET specification and can be found below.
There are two sections in the json, a section for overriding operator output encodings called "activation_encodings" and a section for overriding parameter (weight and bias) encodings called "param_encodings". Both must be present in the file, but can be empty if no overrides are desired.
An example with all of the currently supported options:
{ "activation_encodings": { "Conv1:0": [ { "bitwidth": 8, "max": 12.82344407824954, "min": 0.0, "offset": 0, "scale": 0.050288015993135454 } ], "input:0": [ { "bitwidth": 8, "max": 0.9960872825108046, "min": 1.0039304197656937, "offset": 127, "scale": 0.007843206675594112 } ] }, "param_encodings": { "Conv2d/weights": [ { "bitwidth": 8, "max": 1.700559472933134, "min": 2.1006477158567995, "offset": 140, "scale": 0.01490669485799974 } ] } }
Under "activation_encodings" the names (eg "Conv1:0") represent the output tensor names where quantization should be overriden. Under "param_encodings" the names represent the weights or biases for which the encodings will be specified. A brief breakdown of the common parameters:
Note that it is not required to provide scale (also referred to as delta) and offset (zero point). If they are provided they will be used, otherwise they will be calulated from the provided bitwidth, min, and max parameters.