High Granularity Quantization (HGQ)

Warning

While still supported and maintained, HGQ is deprecated in favor of HGQ2. New projects are strongly encouraged to use HGQ2 instead.

High Granularity Quantization (HGQ) is a library that performs gradient-based automatic bitwidth optimization and quantization-aware training algorithm for neural networks to be deployed on FPGAs. By leveraging gradients, it allows for bitwidth optimization at arbitrary granularity, up to per-weight and per-activation level.

Conversion of models made with HGQ library is fully supported. The HGQ models are first converted to proxy model format, which can then be parsed by hls4ml bit-accurately. Below is an example of how to create a model with HGQ and convert it to hls4ml model.

import keras
from HGQ.layers import HDense, HDenseBatchNorm, HQuantize
from HGQ import ResetMinMax, FreeBOPs

model = keras.models.Sequential([
   HQuantize(beta=1.e-5),
   HDenseBatchNorm(32, beta=1.e-5, activation='relu'),
   HDenseBatchNorm(32, beta=1.e-5, activation='relu'),
   HDense(10, beta=1.e-5),
])

 opt = keras.optimizers.Adam(learning_rate=0.001)
 loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 model.compile(optimizer=opt, loss=loss, metrics=['accuracy'])
 callbacks = [ResetMinMax(), FreeBOPs()]

 model.fit(..., callbacks=callbacks)

 from HGQ import trace_minmax, to_proxy_model
 from hls4ml.converters import convert_from_keras_model

 trace_minmax(model, x_train, cover_factor=1.0)
 proxy = to_proxy_model(model, aggressive=True)

 model_hls = convert_from_keras_model(proxy, backend='vivado',output_dir=... ,part=...)

An interactive example of HGQ can be found in the kaggle notebook. Full documentation can be found at calad0i.github.io/HGQ.