FracBits: Mixed Precision Quantization via Fractional Bit-Widths

• URL: http://arxiv.org/abs/2007.02017v2
• Date: Thu, 3 Dec 2020 03:22:55 GMT
• Title: FracBits: Mixed Precision Quantization via Fractional Bit-Widths
• Authors: Linjie Yang, Qing Jin
• Abstract summary: Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints.
• Score: 29.72454879490227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource constraints can be met during the quantization-aware training which results in an optimized mixed precision model. Further, our method can be naturally combined with channel pruning for better computation cost allocation. Our final models achieve comparable or better performance than previous quantization methods with mixed precision on MobilenetV1/V2, ResNet18 under different resource constraints on ImageNet dataset.

Related papers

• MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search [7.564770908909927]
  Quantization is a technique for creating efficient Deep Neural Networks (DNNs) We propose MixQuant, a search algorithm that finds the optimal custom quantization bit-width for each layer weight based on roundoff error. We show that combining MixQuant with BRECQ, a state-of-the-art quantization method, yields better quantized model accuracy than BRECQ alone.
  arXiv  Detail & Related papers  (2023-09-29T15:49:54Z)
• On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks [52.97107229149988]
  We propose an On-Chip Hardware-Aware Quantization framework, performing hardware-aware mixed-precision quantization on deployed edge devices. For efficiency metrics, we built an On-Chip Quantization Aware pipeline, which allows the quantization process to perceive the actual hardware efficiency of the quantization operator. For accuracy metrics, we propose Mask-Guided Quantization Estimation technology to effectively estimate the accuracy impact of operators in the on-chip scenario.
  arXiv  Detail & Related papers  (2023-09-05T04:39:34Z)
• FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search [50.07268323597872]
  We propose the first one-shot mixed-precision quantization search that eliminates the need for retraining in both integer and low-precision floating point models. With integer models, we increase the accuracy of ResNet-18 on ImageNet by 1.31% and ResNet-50 by 0.90% with equivalent model cost over previous methods. For the first time, we explore a novel mixed-precision floating-point search and improve MobileNetV2 by up to 0.98% compared to prior state-of-the-art FP8 models.
  arXiv  Detail & Related papers  (2023-08-07T04:17:19Z)
• A Practical Mixed Precision Algorithm for Post-Training Quantization [15.391257986051249]
  Mixed-precision quantization is a promising solution to find a better performance-efficiency trade-off than homogeneous quantization. We present a simple post-training mixed precision algorithm that only requires a small unlabeled calibration dataset. We show that we can find mixed precision networks that provide a better trade-off between accuracy and efficiency than their homogeneous bit-width equivalents.
  arXiv  Detail & Related papers  (2023-02-10T17:47:54Z)
• Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search [7.392278887917975]
  Mixed-precision quantization allows different tensors to be quantized to varying levels of numerical precision. We evaluate our method for computer vision and natural language processing and demonstrate latency reductions of up to 27.59% and 34.31%.
  arXiv  Detail & Related papers  (2023-02-02T19:30:00Z)
• Vertical Layering of Quantized Neural Networks for Heterogeneous Inference [57.42762335081385]
  We study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. We can theoretically achieve any precision network for on-demand service while only needing to train and maintain one model.
  arXiv  Detail & Related papers  (2022-12-10T15:57:38Z)
• CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification [51.81850995661478]
  Mixed-precision quantization has been widely applied on deep neural networks (DNNs) Previous attempts on bit-level regularization and pruning-based dynamic precision adjustment during training suffer from noisy gradients and unstable convergence. We propose Continuous Sparsification Quantization (CSQ), a bit-level training method to search for mixed-precision quantization schemes with improved stability.
  arXiv  Detail & Related papers  (2022-12-06T05:44:21Z)
• Combinatorial optimization for low bit-width neural networks [23.466606660363016]
  Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based optimization in a two-stage train-and-compress setting. We show that a combination of greedy coordinate descent and this novel approach can attain competitive accuracy on binary classification tasks.
  arXiv  Detail & Related papers  (2022-06-04T15:02:36Z)
• AMED: Automatic Mixed-Precision Quantization for Edge Devices [3.5223695602582614]
  Quantized neural networks are well known for reducing the latency, power consumption, and model size without significant harm to the performance. Mixed-precision quantization offers better utilization of customized hardware that supports arithmetic operations at different bitwidths.
  arXiv  Detail & Related papers  (2022-05-30T21:23:22Z)
• Bayesian Bits: Unifying Quantization and Pruning [73.27732135853243]
  We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. We experimentally validate our proposed method on several benchmark datasets and show that we can learn pruned, mixed precision networks.
  arXiv  Detail & Related papers  (2020-05-14T16:00:34Z)
• Training with Quantization Noise for Extreme Model Compression [57.51832088938618]
  We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator. In this paper, we extend this approach to work beyond int8 fixed-point quantization with extreme compression methods.
  arXiv  Detail & Related papers  (2020-04-15T20:10:53Z)

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.