Revisiting Cascaded Ensembles for Efficient Inference

• URL: http://arxiv.org/abs/2407.02348v1
• Date: Tue, 2 Jul 2024 15:14:12 GMT
• Title: Revisiting Cascaded Ensembles for Efficient Inference
• Authors: Steven Kolawole, Don Dennis, Ameet Talwalkar, Virginia Smith,
• Abstract summary: A common approach to make machine learning inference more efficient is to use example-specific adaptive schemes. In this work we study a simple scheme for adaptive inference. We build a cascade of ensembles (CoE), beginning with resource-efficient models and growing to larger, more expressive models.
• Score: 32.914852531806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A common approach to make machine learning inference more efficient is to use example-specific adaptive schemes, which route or select models for each example at inference time. In this work we study a simple scheme for adaptive inference. We build a cascade of ensembles (CoE), beginning with resource-efficient models and growing to larger, more expressive models, where ensemble agreement serves as a data-dependent routing criterion. This scheme is easy to incorporate into existing inference pipelines, requires no additional training, and can be used to place models across multiple resource tiers--for instance, serving efficient models at the edge and invoking larger models in the cloud only when necessary. In cases where parallel inference is feasible, we show that CoE can improve accuracy relative to the single best model while reducing the average cost of inference by up to 7x, and provides Pareto-dominate solutions in accuracy and efficiency relative to existing adaptive inference baselines. These savings translate to an over 3x-reduction in total monetary cost when performing inference using a heterogeneous cluster of GPUs. Finally, for edge inference scenarios where portions of the cascade reside at the edge vs. in the cloud, CoE can provide a 14x reduction in communication cost and inference latency without sacrificing accuracy.

Related papers

• Dividable Configuration Performance Learning [4.949726352498762]
  We propose a model-agnostic and sparsity-robust framework for predicting configuration performance, dubbed DaL. DaL is based on the new paradigm of dividable learning that builds a model via "divide-and-learn"
  arXiv  Detail & Related papers  (2024-09-11T21:23:23Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• On Optimal Caching and Model Multiplexing for Large Model Inference [66.50550915522551]
  Large Language Models (LLMs) and other large foundation models have achieved noteworthy success, but their size exacerbates existing resource consumption and latency challenges. We study two approaches for mitigating these challenges: employing a cache to store previous queries and learning a model multiplexer to choose from an ensemble of models for query processing.
  arXiv  Detail & Related papers  (2023-06-03T05:01:51Z)
• Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing [85.74517957717363]
  HALP accelerates inference by designing a seamless collaboration among edge devices (EDs) in Edge Computing. Experiments show that the distributed inference HALP achieves 1.7x inference acceleration for VGG-16. It is shown that the model selection with distributed inference HALP can significantly improve service reliability.
  arXiv  Detail & Related papers  (2022-11-24T19:48:30Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)
• Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models [7.217783736464403]
  Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space. We propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and the weights of a neural network surrogate model.
  arXiv  Detail & Related papers  (2021-08-28T14:31:45Z)
• Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference [22.785214118527872]
  Device-edge co-inference opens up new possibilities for resource-constrained wireless devices to execute deep neural network (DNN)-based applications. We study the joint optimization of the model placement and online model splitting decisions to minimize the energy-and-time cost of device-edge co-inference.
  arXiv  Detail & Related papers  (2021-05-28T06:55:04Z)
• Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity [26.518803984578867]
  Training neural network models with discrete (categorical or structured) latent variables can be computationally challenging. One typically resorts to sampling-based approximations of the true marginal. We propose a new training strategy which replaces these estimators by an exact yet efficient marginalization.
  arXiv  Detail & Related papers  (2020-07-03T19:36:35Z)
• Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference [119.19779637025444]
  Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) This paper studies multi-exit networks associated with input-adaptive inference, showing their strong promise in achieving a "sweet point" in cooptimizing model accuracy, robustness and efficiency.
  arXiv  Detail & Related papers  (2020-02-24T00:40:22Z)

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.