Unified Kernel-Segregated Transpose Convolution Operation

• URL: http://arxiv.org/abs/2502.20493v1
• Date: Thu, 27 Feb 2025 19:56:25 GMT
• Title: Unified Kernel-Segregated Transpose Convolution Operation
• Authors: Vijay Srinivas Tida, Md Imran Hossen, Liqun Shan, Sai Venkatesh Chilukoti, Sonya Hsu, Xiali Hei,
• Abstract summary: We introduce a unified kernel segregation approach that limits the usage of memory and computational resources.<n>The proposed method for the transpose convolution layers in the EB-GAN model demonstrates significant memory savings of up to 35 MB.
• Score: 3.4558311080267954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The optimization of the transpose convolution layer for deep learning applications is achieved with the kernel segregation mechanism. However, kernel segregation has disadvantages, such as computing extra elements to obtain the output feature map with odd dimensions while launching a thread. To mitigate this problem, we introduce a unified kernel segregation approach that limits the usage of memory and computational resources by employing one unified kernel to execute four sub-kernels. The findings reveal that the suggested approach achieves an average computational speedup of 2.03x (3.89x) when tested on specific datasets with an RTX 2070 GPU (Intel Xeon CPU). The ablation study shows an average computational speedup of 3.5x when evaluating the transpose convolution layers from well-known Generative Adversarial Networks (GANs). The implementation of the proposed method for the transpose convolution layers in the EB-GAN model demonstrates significant memory savings of up to 35 MB.

Related papers

• An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks [0.5737287537823071]
  Rotation equivariant graph neural networks yield state-of-the-art performance on spatial deep learning tasks.<n>Key to these models is the Clebsch-Gordon (CG) tensor product, a kernel that contracts two dense feature vectors with a highly structured sparse tensor to produce a dense output vector.<n>We introduce a GPU sparse kernel generator for the CG tensor product that provides significant speedup over the best existing open and closed-source implementations.
  arXiv  Detail & Related papers  (2025-01-23T08:20:47Z)
• Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss [59.835032408496545]
  We propose a tile-based strategy that partitions the contrastive loss calculation into arbitrary small blocks. We also introduce a multi-level tiling strategy to leverage the hierarchical structure of distributed systems. Compared to SOTA memory-efficient solutions, it achieves a two-order-of-magnitude reduction in memory while maintaining comparable speed.
  arXiv  Detail & Related papers  (2024-10-22T17:59:30Z)
• State-Free Inference of State-Space Models: The Transfer Function Approach [132.83348321603205]
  State-free inference does not incur any significant memory or computational cost with an increase in state size. We achieve this using properties of the proposed frequency domain transfer function parametrization. We report improved perplexity in language modeling over a long convolutional Hyena baseline.
  arXiv  Detail & Related papers  (2024-05-10T00:06:02Z)
• Tensor Slicing and Optimization for Multicore NPUs [2.670309629218727]
  This paper proposes a compiler optimization pass for Multicore NPUs, called Slicing Optimization (TSO) TSO identifies the best tensor slicing that minimizes execution time for a set of CNN models. Results show that TSO is capable of identifying the best tensor slicing that minimizes execution time for a set of CNN models.
  arXiv  Detail & Related papers  (2023-04-06T12:03:03Z)
• Advancing Direct Convolution using Convolution Slicing Optimization and ISA Extensions [1.2006896500048552]
  Convolution is one of the most computationally intensive operations that must be performed for machine-learning model inference. This paper proposes SConv: a direct-convolution algorithm based on a MLIR/LLVM code-generation toolchain that can be integrated into machine-learning compilers.
  arXiv  Detail & Related papers  (2023-03-08T17:23:39Z)
• Efficient Dataset Distillation Using Random Feature Approximation [109.07737733329019]
  We propose a novel algorithm that uses a random feature approximation (RFA) of the Neural Network Gaussian Process (NNGP) kernel. Our algorithm provides at least a 100-fold speedup over KIP and can run on a single GPU. Our new method, termed an RFA Distillation (RFAD), performs competitively with KIP and other dataset condensation algorithms in accuracy over a range of large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-21T15:56:13Z)
• Kernel-Segregated Transpose Convolution Operation [2.9822184411723645]
  Transpose convolution layers are computationally intensive due to the increased feature map size due to adding zeros after each element in each row and column. We propose an algorithmic-level optimization technique for the effective transpose convolution implementation to solve these problems.
  arXiv  Detail & Related papers  (2022-09-08T10:42:49Z)
• Distributed Out-of-Memory NMF on CPU/GPU Architectures [1.0051474951635875]
  We propose an efficient out-of-memory implementation of the Non-negative Matrix Factorization (NMF) algorithm for HPC systems. Benchmark results show significant improvement of 32X to 76x speedup with the new implementation using GPU over the CPU-based NMFk.
  arXiv  Detail & Related papers  (2022-02-19T03:49:21Z)
• Efficient and Generic 1D Dilated Convolution Layer for Deep Learning [52.899995651639436]
  We introduce our efficient implementation of a generic 1D convolution layer covering a wide range of parameters. It is optimized for x86 CPU architectures, in particular, for architectures containing Intel AVX-512 and AVX-512 BFloat16 instructions. We demonstrate the performance of our optimized 1D convolution layer by utilizing it in the end-to-end neural network training with real genomics datasets.
  arXiv  Detail & Related papers  (2021-04-16T09:54:30Z)
• Inception Convolution with Efficient Dilation Search [121.41030859447487]
  Dilation convolution is a critical mutant of standard convolution neural network to control effective receptive fields and handle large scale variance of objects. We propose a new mutant of dilated convolution, namely inception (dilated) convolution where the convolutions have independent dilation among different axes, channels and layers. We explore a practical method for fitting the complex inception convolution to the data, a simple while effective dilation search algorithm(EDO) based on statistical optimization is developed.
  arXiv  Detail & Related papers  (2020-12-25T14:58:35Z)
• DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution [136.7261709896713]
  We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. The proposed method achieves promising results on both ScanetNetV2 and S3DIS. It also improves inference speed by more than 25% over the current state-of-the-art.
  arXiv  Detail & Related papers  (2020-11-26T14:56:57Z)

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.