Subtensor Quantization for Mobilenets

• URL: http://arxiv.org/abs/2011.08009v1
• Date: Wed, 4 Nov 2020 15:41:47 GMT
• Title: Subtensor Quantization for Mobilenets
• Authors: Thu Dinh, Andrey Melnikov, Vasilios Daskalopoulos, Sek Chai
• Abstract summary: Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference. In this paper, we analyzed several root causes of quantization loss and proposed alternatives that do not rely on per-channel or training-aware approaches. We evaluate the image classification task on ImageNet dataset, and our post-training quantized 8-bit inference top-1 accuracy in within 0.7% of the floating point version.
• Score: 5.735035463793008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference. However, not all DNN designs are friendly to quantization. For example, the popular Mobilenet architecture has been tuned to reduce parameter size and computational latency with separable depth-wise convolutions, but not all quantization algorithms work well and the accuracy can suffer against its float point versions. In this paper, we analyzed several root causes of quantization loss and proposed alternatives that do not rely on per-channel or training-aware approaches. We evaluate the image classification task on ImageNet dataset, and our post-training quantized 8-bit inference top-1 accuracy in within 0.7% of the floating point version.

Related papers

• Two Heads are Better Than One: Neural Networks Quantization with 2D Hilbert Curve-based Output Representation [3.4606942690643336]
  We introduce a novel approach for DNN quantization that uses a redundant representation of DNN's output. We demonstrate that this mapping can reduce quantization error. Our approach can be applied to other tasks, including segmentation, object detection, and key-points prediction.
  arXiv  Detail & Related papers  (2024-05-22T21:59:46Z)
• FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation [2.4149105714758545]
  We propose a novel framework referred to as the Fixed-Point Quantizer of deep neural Networks (FxP-QNet) FxP-QNet 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. Results show that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall memory requirements of their full-precision counterparts by 7.16x, 10.36x, and 6.44x with less than 0.95%, 0.95%, and 1.99%
  arXiv  Detail & Related papers  (2022-03-22T23:01:43Z)
• OMPQ: Orthogonal Mixed Precision Quantization [64.59700856607017]
  Mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization. We propose to optimize a proxy metric, the concept of networkity, which is highly correlated with the loss of the integer programming. This approach reduces the search time and required data amount by orders of magnitude, with little compromise on quantization accuracy.
  arXiv  Detail & Related papers  (2021-09-16T10:59:33Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search [112.05977301976613]
  We propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides. We first propose the joint training of architecture and quantization with a shared step size to acquire a large number of quantized models. Then a bit-inheritance scheme is introduced to transfer the quantized models to the lower bit, which further reduces the time cost and improves the quantization accuracy.
  arXiv  Detail & Related papers  (2020-10-09T03:52:16Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)
• Efficient Integer-Arithmetic-Only Convolutional Neural Networks [87.01739569518513]
  We replace conventional ReLU with Bounded ReLU and find that the decline is due to activation quantization. Our integer networks achieve equivalent performance as the corresponding FPN networks, but have only 1/4 memory cost and run 2x faster on modern GPU.
  arXiv  Detail & Related papers  (2020-06-21T08:23:03Z)
• Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation [4.638764944415326]
  Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput. We focus on quantization techniques that are amenable to acceleration by processors with high- throughput integer math pipelines. We present a workflow for 8-bit quantization that is able to maintain accuracy within 1% of the floating-point baseline on all networks studied.
  arXiv  Detail & Related papers  (2020-04-20T19:59:22Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)
• Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks [13.929168096016957]
  We introduce a novel methodology for training deep neural networks using 8-bit floating point (FP8) numbers. Reduced bit precision allows for a larger effective memory and increased computational speed. We show that, unlike previous 8-bit precision training methods, the proposed method works out-of-the-box for representative models.
  arXiv  Detail & Related papers  (2020-01-16T06:38:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.