Minimal Filtering Algorithms for Convolutional Neural Networks

• URL: http://arxiv.org/abs/2004.05607v1
• Date: Sun, 12 Apr 2020 13:18:25 GMT
• Title: Minimal Filtering Algorithms for Convolutional Neural Networks
• Authors: Aleksandr Cariow and Galina Cariowa
• Abstract summary: We develop fully parallel hardware-oriented algorithms for implementing the basic filtering operation for M=3,5,7,9, and 11. A fully parallel hardware implementation of the proposed algorithms in each case gives approximately 30 percent savings in the number of embedded multipliers.
• Score: 82.24592140096622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present several resource-efficient algorithmic solutions regarding the fully parallel hardware implementation of the basic filtering operation performed in the convolutional layers of convolution neural networks. In fact, these basic operations calculate two inner products of neighboring vectors formed by a sliding time window from the current data stream with an impulse response of the M-tap finite impulse response filter. We used Winograd minimal filtering trick and applied it to develop fully parallel hardware-oriented algorithms for implementing the basic filtering operation for M=3,5,7,9, and 11. A fully parallel hardware implementation of the proposed algorithms in each case gives approximately 30 percent savings in the number of embedded multipliers compared to a fully parallel hardware implementation of the naive calculation methods.

Related papers

• DeepPCR: Parallelizing Sequential Operations in Neural Networks [4.241834259165193]
  We introduce DeepPCR, a novel algorithm which parallelizes typically sequential operations in order to speed up inference and training of neural networks. DeepPCR is based on interpreting a sequence of $L$ steps as the solution of a specific system of equations, which we recover using the Parallel Cyclic Reduction algorithm. To verify the theoretical lower complexity of the algorithm, and to identify regimes for speedup, we test the effectiveness of DeepPCR in parallelizing the forward and backward pass in multi-layer perceptrons.
  arXiv  Detail & Related papers  (2023-09-28T10:15:30Z)
• Low-complexity Approximate Convolutional Neural Networks [1.7368964547487395]
  We present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet) The idea is to approximate all elements of a given ConvNet with efficient approximations capable of extreme reductions in computational complexity. Such low-complexity structures pave the way for low-power, efficient hardware designs.
  arXiv  Detail & Related papers  (2022-07-29T21:59:29Z)
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [67.33850633281803]
  We present a versatile new input encoding that permits the use of a smaller network without sacrificing quality. A small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through a gradient descent. We achieve a combined speed of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds.
  arXiv  Detail & Related papers  (2022-01-16T07:22:47Z)
• FPGA deep learning acceleration based on convolutional neural network [0.0]
  This paper proposes a convolutional neural network hardware accelerator based on field programmable logic gate array (FPGA) The energy efficiency ratio of the accelerator proposed in this article reaches 32.73 GOPS/W, which is 34% higher than the existing solution, and the performance reaches 317.86 GOPS.
  arXiv  Detail & Related papers  (2020-11-17T16:20:44Z)
• NOMA in UAV-aided cellular offloading: A machine learning approach [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T17:38:48Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)
• Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving [106.63673243937492]
  Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. We frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point method, as well as hybrid methods of both. Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallelizable iterations, and hence reduced time given sufficient parallel computing power.
  arXiv  Detail & Related papers  (2020-02-10T10:11:31Z)
• Channel Assignment in Uplink Wireless Communication using Machine Learning Approach [54.012791474906514]
  This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. Due to high computational complexity, machine learning approaches are employed to obtain computational efficient solutions.
  arXiv  Detail & Related papers  (2020-01-12T15:54:20Z)

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.