ALigN: A Highly Accurate Adaptive Layerwise Log_2_Lead Quantization of Pre-Trained Neural Networks

Abstract

Deep Neural Networks are one of the machine learning techniques which are increasingly used in a variety of applications. However, the significantly high memory and computation demands of deep neural networks often limit their deployment on embedded systems. Many recent works have considered this problem by proposing different types of data quantization schemes. However, most of these techniques either require post-quantization retraining of deep neural networks or bear a significant loss in output accuracy. In this paper, we propose a novel and scalable technique with two different modes for the quantization of the parameters of pre-trained neural networks. In the first mode, referred to as log_2_lead, we use a single template for the quantization of all parameters. In the second mode, denoted as ALigN, we analyze the trained parameters of each layer and adaptively adjust the quantization template to achieve even higher accuracy. Our technique significantly maintains the accuracy of the parameters and does not require retraining of the networks. Moreover, it supports quantization to an arbitrary bit-size. For example, compared to the single-precision floating-point numbers-based implementation, our proposed 8bit quantization technique generates only ∼ 0.2% and ∼ 0.1%, loss in the Top-1 and Top-5 accuracies respectively for VGG-16 network using ImageNet dataset. We have observed similar minimal losses in the Top-1 and Top-5 accuracies for AlexNet and Resnet-18 using the proposed quantization scheme for the 8-bit range. Our proposed quantization technique also provides a higher mean intersection over union for semantic segmentation when compared with state-of-the-art quantization techniques. The proposed technique represents parameters in powers of 2, thereby eliminating the need for resource-computationally intensive multiplier units for the hardware accelerators of the neural networks. We also present a design for implementing the multiplication operation using bit-shifts and addition for the proposed quantization technique.

ExPAN(N)D: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-Based Systems

“…For example, for the quantization of pre-trained DNNs, [9], [28]- [30] have proposed different schemes. The techniques presented in [29], [30] have focused on the utilization of logarithmic data representations to avoid the computationally expensive multiplication operations. However, some recent works, such as [31]- [33] have utilized fixed-point quantization schemes to employ the well-explored high-performance and energy-efficient ap-proximate adders and multipliers.…”