Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks

• URL: http://arxiv.org/abs/2407.01054v2
• Date: Tue, 24 Sep 2024 07:06:26 GMT
• Title: Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
• Authors: Beatrice Alessandra Motetti, Matteo Risso, Alessio Burrello, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari,
• Abstract summary: deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization. We propose a novel methodology to apply them jointly via a lightweight gradient-based search.
• Score: 10.229120811024162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization, which lead to latency and memory occupation improvements. These optimization techniques are usually applied independently. We propose a novel methodology to apply them jointly via a lightweight gradient-based search, and in a hardware-aware manner, greatly reducing the time required to generate Pareto-optimal DNNs in terms of accuracy versus cost (i.e., latency or memory). We test our approach on three edge-relevant benchmarks, namely CIFAR-10, Google Speech Commands, and Tiny ImageNet. When targeting the optimization of the memory footprint, we are able to achieve a size reduction of 47.50% and 69.54% at iso-accuracy with the baseline networks with all weights quantized at 8 and 2-bit, respectively. Our method surpasses a previous state-of-the-art approach with up to 56.17% size reduction at iso-accuracy. With respect to the sequential application of state-of-the-art pruning and mixed-precision optimizations, we obtain comparable or superior results, but with a significantly lowered training time. In addition, we show how well-tailored cost models can improve the cost versus accuracy trade-offs when targeting specific hardware for deployment.

Related papers

• 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)
• Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge [17.277918711842457]
  Mixed-precision quantization offers the opportunity to optimize the trade-offs between model size, latency, and statistical accuracy. This paper proposes a hybrid search methodology to navigate the search space of mixed-precision configurations for a given network. It consists of a hardware-agnostic differentiable search algorithm followed by a hardware-aware optimization to find mixed-precision configurations latency-optimized for a specific hardware target.
  arXiv  Detail & Related papers  (2023-07-06T09:57:48Z)
• Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA [0.0]
  Layer-wise mixed-precision quantization allows for more efficient results while inflating the design space. We present an in-depth quantitative methodology to efficiently explore the design space considering the limited hardware resources of a given FPGA. Our resulting hardware accelerators implement truly mixed-precision operations that enable efficient execution of layer-wise and channel-wise quantized CNNs.
  arXiv  Detail & Related papers  (2022-08-09T15:32:51Z)
• 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)
• 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)
• Multi-Exit Semantic Segmentation Networks [78.44441236864057]
  We propose a framework for converting state-of-the-art segmentation models to MESS networks. specially trained CNNs that employ parametrised early exits along their depth to save during inference on easier samples. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the device capabilities and application-specific requirements.
  arXiv  Detail & Related papers  (2021-06-07T11:37:03Z)
• 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)
• APQ: Joint Search for Network Architecture, Pruning and Quantization Policy [49.3037538647714]
  We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and quantization policy, we optimize them in a joint manner. With the same accuracy, APQ reduces the latency/energy by 2x/1.3x over MobileNetV2+HAQ.
  arXiv  Detail & Related papers  (2020-06-15T16:09:17Z)
• Automatic Pruning for Quantized Neural Networks [35.2752928147013]
  We propose an effective pruning strategy for selecting redundant low-precision filters. We conduct extensive experiments on CIFAR-10 and ImageNet with various architectures and precisions. For ResNet-18 on ImageNet, we prune 26.12% of the model size with Binarized Neural Network quantization.
  arXiv  Detail & Related papers  (2020-02-03T01:10:13Z)

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.