SA-MLP: Enhancing Point Cloud Classification with Efficient Addition and Shift Operations in MLP Architectures

• URL: http://arxiv.org/abs/2409.01998v1
• Date: Tue, 3 Sep 2024 15:43:44 GMT
• Title: SA-MLP: Enhancing Point Cloud Classification with Efficient Addition and Shift Operations in MLP Architectures
• Authors: Qiang Zheng, Chao Zhang, Jian Sun,
• Abstract summary: Traditional neural networks heavily rely on multiplication operations, which are computationally expensive. We propose Add-MLP and Shift-MLP, which replace multiplications with addition and shift operations, respectively, significantly enhancing computational efficiency. This study offers an efficient and effective solution for point cloud classification, balancing performance with computational efficiency.
• Score: 46.266960248570086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study addresses the computational inefficiencies in point cloud classification by introducing novel MLP-based architectures inspired by recent advances in CNN optimization. Traditional neural networks heavily rely on multiplication operations, which are computationally expensive. To tackle this, we propose Add-MLP and Shift-MLP, which replace multiplications with addition and shift operations, respectively, significantly enhancing computational efficiency. Building on this, we introduce SA-MLP, a hybrid model that intermixes alternately distributed shift and adder layers to replace MLP layers, maintaining the original number of layers without freezing shift layer weights. This design contrasts with the ShiftAddNet model from previous literature, which replaces convolutional layers with shift and adder layers, leading to a doubling of the number of layers and limited representational capacity due to frozen shift weights. Moreover, SA-MLP optimizes learning by setting distinct learning rates and optimizers specifically for the adder and shift layers, fully leveraging their complementary strengths. Extensive experiments demonstrate that while Add-MLP and Shift-MLP achieve competitive performance, SA-MLP significantly surpasses the multiplication-based baseline MLP model and achieves performance comparable to state-of-the-art MLP-based models. This study offers an efficient and effective solution for point cloud classification, balancing performance with computational efficiency.

Related papers

• R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference [77.47238561728459]
  R-Sparse is a training-free activation sparsity approach capable of achieving high sparsity levels in advanced LLMs. Experiments on Llama-2/3 and Mistral models across ten diverse tasks demonstrate that R-Sparse achieves comparable performance at 50% model-level sparsity.
  arXiv  Detail & Related papers  (2025-04-28T03:30:32Z)
• Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models [2.3949320404005436]
  Particle Swarm Optimization and Large Language Models (LLMs) have been individually applied in optimization and deep learning. Our work addresses this gap by integrating LLMs into PSO to reduce model evaluations and improve convergence. Our method speeds up search space exploration by substituting underperforming particle placements with best suggestions.
  arXiv  Detail & Related papers  (2025-04-19T00:54:59Z)
• Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure [8.29566258132752]
  This paper investigates a pruning method for anomaly detection in resource-constrained environments, specifically targeting EVCI. optimized models achieved significant reductions in model size and inference times, with only a marginal impact on their performance. Notably, our findings indicate that, in the context of EVCI, pruning and FS can enhance computational efficiency while retaining critical anomaly detection capabilities.
  arXiv  Detail & Related papers  (2025-03-19T00:18:37Z)
• Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensors [7.630782404476683]
  We introduce a novel tokenization approach, referred to as Sensor Snapshot Tokenization (SST), which preserves variable-specific representations of power delay profile ( PDP) We also propose a lightweight Swish-Gated Linear Unit-based Transformer (L-SwiGLU Transformer) model, designed to reduce computational complexity without compromising localization accuracy.
  arXiv  Detail & Related papers  (2025-01-14T01:16:30Z)
• OP-LoRA: The Blessing of Dimensionality [93.08208871549557]
  Low-rank adapters enable fine-tuning of large models with only a small number of parameters. They often pose optimization challenges, with poor convergence. We introduce an over- parameterized approach that accelerates training without increasing inference costs. We achieve improvements in vision-language tasks and especially notable increases in image generation.
  arXiv  Detail & Related papers  (2024-12-13T18:55:19Z)
• Scaling Laws for Predicting Downstream Performance in LLMs [75.28559015477137]
  This work focuses on the pre-training loss as a more computation-efficient metric for performance estimation. We present FLP-M, a fundamental approach for performance prediction that addresses the practical need to integrate datasets from multiple sources during pre-training.
  arXiv  Detail & Related papers  (2024-10-11T04:57:48Z)
• GERA: Geometric Embedding for Efficient Point Registration Analysis [20.690695788384517]
  We propose a novel point cloud registration network that leverages a pure geometric architecture, constructing geometric information offline. Our method is the first to replace 3D coordinate inputs with offline-constructed geometric encoding, improving generalization and stability.
  arXiv  Detail & Related papers  (2024-10-01T11:19:56Z)
• A deeper look at depth pruning of LLMs [49.30061112976263]
  Large Language Models (LLMs) are resource-intensive to train but more costly to deploy in production. Recent work has attempted to prune blocks of LLMs based on cheap proxies for estimating block importance. We show that adaptive metrics exhibit a trade-off in performance between tasks.
  arXiv  Detail & Related papers  (2024-07-23T08:40:27Z)
• MLP Can Be A Good Transformer Learner [73.01739251050076]
  Self-attention mechanism is the key of the Transformer but often criticized for its computation demands. This paper introduces a novel strategy that simplifies vision transformers and reduces computational load through the selective removal of non-essential attention layers.
  arXiv  Detail & Related papers  (2024-04-08T16:40:15Z)
• NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning [40.994306592119266]
  Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. Some general approaches (e.g. quantization and distillation) have been widely studied to reduce the compute/memory of PLM fine-tuning. We propose to coin a lightweight PLM through NTK-approximating modules in fusion.
  arXiv  Detail & Related papers  (2023-07-18T03:12:51Z)
• Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation [68.24659910441736]
  Shifted-Pillars-Concatenation (SPC) module offers superior local modeling power and performance gains. We build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet. Experiments show Caterpillar's excellent performance on both small-scale and ImageNet-1k classification benchmarks.
  arXiv  Detail & Related papers  (2023-05-28T06:19:36Z)
• Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation [32.645022002807416]
  Multi-layer perceptron (MLP) network has regained popularity among researchers due to their comparable results to ViT. We propose a novel permutable hybrid network for Vol-MedSeg, named PHNet, which capitalizes on the strengths of both convolution neural networks (CNNs) and PHNet.
  arXiv  Detail & Related papers  (2023-03-23T08:59:09Z)
• The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers [59.87030906486969]
  This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. We show that sparsity is a prevalent phenomenon that occurs for both natural language processing and vision tasks. We discuss how sparsity immediately implies a way to significantly reduce the FLOP count and improve efficiency for Transformers.
  arXiv  Detail & Related papers  (2022-10-12T15:25:19Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Efficient Language Modeling with Sparse all-MLP [53.81435968051093]
  All-MLPs can match Transformers in language modeling, but still lag behind in downstream tasks. We propose sparse all-MLPs with mixture-of-experts (MoEs) in both feature and input (tokens) We evaluate its zero-shot in-context learning performance on six downstream tasks, and find that it surpasses Transformer-based MoEs and dense Transformers.
  arXiv  Detail & Related papers  (2022-03-14T04:32:19Z)
• Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs [84.3235981545673]
  Token-mixing multi-layer perceptron (MLP) models have shown competitive performance in computer vision tasks. We present Mix-Shift-MLP which makes the size of the local receptive field used for mixing increase with respect to the amount of spatial shifting. MS-MLP achieves competitive performance in multiple vision benchmarks.
  arXiv  Detail & Related papers  (2022-02-14T06:53:48Z)
• Using Fitness Dependent Optimizer for Training Multi-layer Perceptron [13.280383503879158]
  This study presents a novel training algorithm depending upon the recently proposed Fitness Dependent (FDO) The stability of this algorithm has been verified and performance-proofed in both the exploration and exploitation stages. The proposed approach using FDO as a trainer can outperform the other approaches using different trainers on the dataset.
  arXiv  Detail & Related papers  (2022-01-03T10:23:17Z)
• Sparse-MLP: A Fully-MLP Architecture with Conditional Computation [7.901786481399378]
  Mixture-of-Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost. We propose Sparse-MLP, scaling the recent-Mixer model with MoE, to achieve a more-efficient architecture.
  arXiv  Detail & Related papers  (2021-09-05T06:43:08Z)
• MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation [40.20599070308035]
  Transformer-based models require quadratic memory and time complexity to the sequence length, making it difficult to extract the long-term interest of users. MLP-based models, renowned for their linear memory and time complexity, have recently shown competitive results compared to Transformer in various tasks. We propose the Multi-Order Interaction layer, which is capable of expressing an arbitrary order of interactions while maintaining the memory and time complexity of the layer.
  arXiv  Detail & Related papers  (2021-08-17T08:38:49Z)

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.