The process of inference is computation intensive and requires a high memory bandwidth to satisfy the low-latency and high-throughput requirement of edge applications.
Quantization and channel pruning techniques are employed to address these issues while achieving high performance and high energy efficiency with little degradation in accuracy. Quantization makes it possible to use integer computing units and to represent weights and activations by lower bits, while pruning reduces the overall required operations. In the Vitis™ AI quantizer, only the quantization tool is included. The pruning tool is packaged in the Vitis AI optimizer. Contact the support team for the Vitis AI development kit if you require the pruning tool.
Generally, 32-bit floating-point weights and activation values are used when training neural networks. By converting the 32-bit floating-point weights and activations to 8-bit integer (INT8) format, the Vitis AI quantizer can reduce computing complexity without losing prediction accuracy. The fixed-point network model requires less memory bandwidth, thus providing faster speed and higher power efficiency than the floating-point model. The Vitis AI quantizer supports common layers in neural networks, such as convolution, pooling, fully connected, and batchnorm.
The Vitis AI quantizer now supports TensorFlow, Pytorch and Caffe (the quantizer names are vai_q_tensorflow, vai_q_pytorch, and vai_q_caffe respectively). The vai_q_tensorflow quantizer is based on Tensorflow 1.15. The vai_q_pytorch quantizer supports Pytorch from 1.1-1.4. The vai_q_tensorflow and vai_q_caffe quantizer support the quantize finetuning feature, but vai_q_pytorch does not.
In the quantize calibration process, only a small set of unlabeled images are required to analyze the distribution of activations. The running time of quantize calibration varies from a few seconds to several minutes, depending on the size of the neural network. Generally, there is a little decline in accuracy after quantization. However, for some networks such as Mobilenets, the accuracy loss might be large. In this situation, quantize finetuning can be used to further improve the accuracy of quantized models. Quantize finetuning requires the original train dataset. According to experiments, several epochs of finetuning are needed and the finetune time varies from several minutes to several hours.