FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs With Dynamic Fixed-Point Representation

Abstract

Deep neural networks (DNNs) have demonstrated their effectiveness in a wide range of computer vision tasks, with the state-of-the-art results obtained through complex and deep structures that require intensive computation and memory. In the past, graphic processing units enabled these breakthroughs because of their greater computational speed. Now-a-days, efficient model inference is crucial for consumer applications on resource-constrained platforms. As a result, there is much interest in the research and development of dedicated deep learning (DL) hardware to improve the throughput and energy efficiency of DNNs. Low-precision representation of DNN data-structures through quantization would bring great benefits to specialized DL hardware especially when expensive floating-point operations can be avoided and replaced by more efficient fixed-point operations. However, the rigorous quantization leads to a severe accuracy drop. As such, quantization opens a large hyper-parameter space at bit-precision levels, the exploration of which is a major challenge. In this paper, we propose a novel framework referred to as the Fixed-Point Quantizer of deep neural Networks (FxP-QNet) that flexibly designs a mixed low-precision DNN for integer-arithmetic-only deployment. Specifically, the FxP-QNet gradually adapts the quantization level for each data-structure of each layer based on the trade-off between the network accuracy and the low-precision requirements. Additionally, it employs post-training self-distillation and network prediction error statistics to optimize the quantization of floating-point values into fixed-point numbers. Examining FxP-QNet 1 on state-of-the-art architectures and the benchmark ImageNet dataset, we empirically demonstrate the effectiveness of FxP-QNet in achieving the accuracy-compression trade-off without the need for training. The results show that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall memory requirements of their full-precision counterparts by 7.16ˆ, 10.36ˆ, and 6.44ˆwith less than 0.95%, 0.95%, and 1.99% accuracy drop, respectively.

Optimization of FPGA-based CNN accelerators using metaheuristics

“…For example, the values of FMs and weight parameters are originally represented as 32-bit floating-point numbers. However, it has been demonstrated that fewer bits can be used to represent these values without a noticeable accuracy drop [25]. This reduces the hardware requirements for CNN implementation as well as reduces the inference latency.…”

MOHAQ: Multi-Objective Hardware-Aware Quantization of recurrent neural networks

“…Several other quantization methods applied optimization algorithms on quantized NN models but without any hardware model guidance [7,[50][51][52][53]. For instance, Loss Aware Post-training quantization (LAPQ) is a layer-wise iterative optimization algorithm to calculate the optimum quantization step for clipping [7].…”

“…This assumption simplifies the computation of the overall sensitivity for different quantization configurations in the search space. Shawahna et al proposed post-training self-distillation and network prediction error function to search for optimal mixed-precision configuration for neural networks [51]. The method progressively quantizes the model to maximize the compression rate under a predefined accuracy threshold.…”