A Simple Packing Algorithm for Optimized Mapping of Artificial Neural Networks onto Non-Volatile Memory Cross-Bar Arrays

• URL: http://arxiv.org/abs/2411.04814v1
• Date: Thu, 07 Nov 2024 15:50:42 GMT
• Title: A Simple Packing Algorithm for Optimized Mapping of Artificial Neural Networks onto Non-Volatile Memory Cross-Bar Arrays
• Authors: W. Haensch,
• Abstract summary: Neuromorphic computing with crossbar arrays has emerged as a promising alternative to improve computing efficiency for machine learning. In this paper, we explore the impact of mapping the layers of an artificial neural network onto physical cross-bar arrays arranged in tiles across a chip. We have developed a simplified mapping algorithm to determine the number of physical tiles, with fixed optimal array dimensions, and to estimate the minimum area occupied by these tiles for a given design objective.
• Score: 0.0
• License:
• Abstract: Neuromorphic computing with crossbar arrays has emerged as a promising alternative to improve computing efficiency for machine learning. Previous work has focused on implementing crossbar arrays to perform basic mathematical operations. However, in this paper, we explore the impact of mapping the layers of an artificial neural network onto physical cross-bar arrays arranged in tiles across a chip. We have developed a simplified mapping algorithm to determine the number of physical tiles, with fixed optimal array dimensions, and to estimate the minimum area occupied by these tiles for a given design objective. This simplified algorithm is compared with conventional binary linear optimization, which solves the equivalent bin-packing problem. We have found that the optimum solution is not necessarily related to the minimum number of tiles; rather, it is shown to be an interaction between tile array capacity and the scaling properties of its peripheral circuits. Additionally, we have discovered that square arrays are not always the best choice for optimal mapping, and that performance optimization comes at the cost of total tile area

Related papers

• SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization [24.55623897747344]
  Neural network pruning is a key technique towards engineering large yet scalable, interpretable, generalizable models. We show how many existing differentiable pruning techniques can be understood as non regularization for group sparse optimization. We propose SequentialAttention++, which advances state the art in large-scale neural network block-wise pruning tasks on the ImageNet and Criteo datasets.
  arXiv  Detail & Related papers  (2024-02-27T21:42:18Z)
• Learning the Positions in CountSketch [49.57951567374372]
  We consider sketching algorithms which first compress data by multiplication with a random sketch matrix, and then apply the sketch to quickly solve an optimization problem. In this work, we propose the first learning-based algorithms that also optimize the locations of the non-zero entries.
  arXiv  Detail & Related papers  (2023-06-11T07:28:35Z)
• Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods [75.34939761152587]
  Efficient computation of the optimal transport distance between two distributions serves as an algorithm that empowers various applications. This paper develops a scalable first-order optimization-based method that computes optimal transport to within $varepsilon$ additive accuracy.
  arXiv  Detail & Related papers  (2023-01-30T15:46:39Z)
• Asymmetric Scalable Cross-modal Hashing [51.309905690367835]
  Cross-modal hashing is a successful method to solve large-scale multimedia retrieval issue. We propose a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues. Our ASCMH outperforms the state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-07-26T04:38:47Z)
• Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithms [9.245687221460654]
  Motivated by applications in single-cell biology and metagenomics, we investigate the problem of matrixing based on a noisy monotone Toeplitz matrix model. We establish fundamental statistical limit for this problem in a decision-theoretic framework and demonstrate that a constrained least squares rate. To address this, we propose a novel-time adaptive sorting algorithm with guaranteed performance improvement.
  arXiv  Detail & Related papers  (2022-01-17T14:53:52Z)
• Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity [15.023842222803058]
  Current algorithms are designed using the block coordinate descent method or the proximal point algorithm. We propose a novel algorithm based on the two-metric projection method, incorporating a carefully designed search direction and variable partitioning scheme. Experimental results on synthetic and real-world datasets demonstrate that our proposed algorithm provides a significant improvement in computational efficiency compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-03T14:39:10Z)
• Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation [71.27989298860481]
  We address the non- optimisation problem of finding a matrix on the Stiefel manifold that maximises a quadratic objective function. We propose a simple yet effective sparsity-promoting algorithm for finding the dominant eigenspace matrix.
  arXiv  Detail & Related papers  (2021-09-30T19:17:35Z)
• Hybrid Trilinear and Bilinear Programming for Aligning Partially Overlapping Point Sets [85.71360365315128]
  In many applications, we need algorithms which can align partially overlapping point sets are invariant to the corresponding corresponding RPM algorithm. We first show that the objective is a cubic bound function. We then utilize the convex envelopes of trilinear and bilinear monomial transformations to derive its lower bound. We next develop a branch-and-bound (BnB) algorithm which only branches over the transformation variables and runs efficiently.
  arXiv  Detail & Related papers  (2021-01-19T04:24:23Z)
• Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix [57.11971786407279]
  Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data. This paper proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix. Our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base.
  arXiv  Detail & Related papers  (2020-08-31T12:28:40Z)
• Combinatorial Black-Box Optimization with Expert Advice [28.45580493454314]
  We consider the problem of black-box function optimization over the hypercube. We propose a computationally efficient model learning algorithm based on multilinears and exponential weight updates.
  arXiv  Detail & Related papers  (2020-06-06T20:26:19Z)

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.