Multiway Storage Modification Machines

• URL: http://arxiv.org/abs/2111.06757v1
• Date: Fri, 12 Nov 2021 15:06:48 GMT
• Title: Multiway Storage Modification Machines
• Authors: J.-M. Chauvet
• Abstract summary: We present a parallel version of Sch"onhage's Storage Modification Machine, the Multiway Storage Modification Machine (MWSMM) Like the alternative Association Storage Modification Machine of Tromp van Emde Boas, MWSMMs recognize in time what Turing Machines recognize in space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a parallel version of Sch\"onhage's Storage Modification Machine, the Multiway Storage Modification Machine (MWSMM). Like the alternative Association Storage Modification Machine of Tromp and van Emde Boas, MWSMMs recognize in polynomial time what Turing Machines recognize in polynomial space. Falling thus into the Second Machine Class, the MWSMM is a parallel machine model conforming to the Parallel Computation Thesis. We illustrate MWSMMs by a simple implementation of Wolfram's String Substitution System.

Related papers

• Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models [92.36510016591782]
  We present a method that is able to distill a pretrained Transformer architecture into alternative architectures such as state space models (SSMs) Our method, called MOHAWK, is able to distill a Mamba-2 variant based on the Phi-1.5 architecture using only 3B tokens and a hybrid version (Hybrid Phi-Mamba) using 5B tokens. Despite using less than 1% of the training data typically used to train models from scratch, Phi-Mamba boasts substantially stronger performance compared to all past open-source non-Transformer models.
  arXiv  Detail & Related papers  (2024-08-19T17:48:11Z)
• Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training [78.93900796545523]
  Mini-Sequence Transformer (MsT) is a methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input sequences and iteratively processes mini-sequences to reduce intermediate memory usage. integrated with the huggingface library, MsT successfully extends the maximum context length of Qwen, Mistral, and Gemma-2 by 12-24x.
  arXiv  Detail & Related papers  (2024-07-22T01:52:30Z)
• MoEUT: Mixture-of-Experts Universal Transformers [75.96744719516813]
  Universal Transformers (UTs) have advantages over standard Transformers in learning compositional generalizations. Layer-sharing drastically reduces the parameter count compared to the non-shared model with the same dimensionality. No previous work has succeeded in proposing a shared-layer Transformer design that is competitive in parameter count-dominated tasks such as language modeling.
  arXiv  Detail & Related papers  (2024-05-25T03:24:32Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• Multithreaded parallelism for heterogeneous clusters of QPUs [0.0]
  We present MILQ, a quantum unrelated parallel machines scheduler and cutter. It prioritizes the total execution time of a batch of circuits scheduled on multiple quantum devices. Our results show a total improvement of up to 26 % compared to a baseline approach.
  arXiv  Detail & Related papers  (2023-11-29T09:54:04Z)
• Support matrix machine: A review [0.0]
  Support matrix machine (SMM) represents one of the emerging methodologies tailored for handling matrix input data. This article provides the first in-depth analysis of the development of the SMM model. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models.
  arXiv  Detail & Related papers  (2023-10-30T16:46:23Z)
• Least Squares Maximum and Weighted Generalization-Memorization Machines [14.139758779594667]
  We propose a new way of remembering by introducing a memory influence mechanism for the least squares support vector machine (LSSVM) The maximum memory impact model (MIMM) and the weighted impact memory model (WIMM) are then proposed.
  arXiv  Detail & Related papers  (2023-08-31T04:48:59Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• Over-the-Air Split Machine Learning in Wireless MIMO Networks [56.27831295707334]
  In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication.
  arXiv  Detail & Related papers  (2022-10-07T15:39:11Z)
• Compiling Turing Machines into Storage Modification Machines [0.0]
  It is well known that Sch"onhage's Storage Modification Machines (SMM) can simulate Turing Machines (TM) We propose a simple transformation of TM into SMM, setting the base for a straightforward TM-to-SMM compiler.
  arXiv  Detail & Related papers  (2021-09-28T10:38:05Z)
• Counterfactual Explanations for Machine Learning on Multivariate Time Series Data [0.9274371635733836]
  This paper proposes a novel explainability technique for providing counterfactual explanations for supervised machine learning frameworks. The proposed method outperforms state-of-the-art explainability methods on several different ML frameworks and data sets in metrics such as faithfulness and robustness.
  arXiv  Detail & Related papers  (2020-08-25T02:04:59Z)

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.