Recent years have seen a paradigm shift towards multi-task learning. This calls for memory and energy-efficient solutions for inference in a multi-task scenario. We propose an algorithm-hardware co-design approach called MIME. MIME reuses the weight parameters of a trained parent task and learns task-specific threshold parameters for inference on multiple child tasks. We find that MIME results in highly memory-efficient DRAM storage of neural-network parameters for multiple tasks compared to conventional multi-task inference. In addition, MIME results in input-dependent dynamic neuronal pruning, thereby enabling energy-efficient inference with higher throughput on a systolic-array hardware. Our experiments with benchmark datasets (child tasks)- CIFAR10, CIFAR100, and Fashion-MNIST, show that MIME achieves ~3.48x memory-efficiency and ~2.4-3.1x energy-savings compared to conventional multi-task inference in Pipelined task mode.
In this work, we propose a graph-adaptive pruning (GAP) method for efficient inference of convolutional neural networks (CNNs). In this method, the network is viewed as a computational graph, in which the vertices denote the computation nodes and edges represent the information flow. Through topology analysis, GAP is capable of adapting to different network structures, especially the widely used cross connections and multi-path data flow in recent novel convolutional models. The models can be adaptively pruned at vertex-level as well as edge-level without any post-processing, thus GAP can dire…
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）
Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either single task model pruning or simply adapting it to multitask scenario, and still face the following problems when handling multitask pruning: 1) Due to the task mismatch, a well-pruned backbone for classification task focuses on preserving filters that can extract category-sensitive information, causing filters that may be useful for other tasks to be pruned during the backbone pruning stage; 2) For multitask predic…
Cut-ViT: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency
Nanjing University of Science and Technology（南京理工大学）；State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery（先进工程机械智能制造国家重点实验室）；Zhejiang University（浙江大学）；Nanjing Normal University（南京师范大学）
Adapting pre-trained neural models to downstream tasks has become the standard practice for obtaining high-quality models. In this work, we propose a novel model adaptation paradigm, adapting by pruning, which prunes neural connections in the pre-trained model to optimise the performance on the target task; all remaining connections have their weights intact. We formulate adapting-by-pruning as an optimisation problem with a differentiable loss and propose an efficient algorithm to prune the model. We prove that the algorithm is near-optimal under standard assumptions and apply the algorithm t…
Compact Bayesian Neural Networks via pruned MCMC sampling
ITTC ARC Centre for Data Analytics for Resources and Environment（ITTC ARC资源与环境数据分析中心）；University of Sydney（悉尼大学）；UNSW Data Science Hub（新南威尔士大学数据科学中心）；University of New South Wales（新南威尔士大学）；Geocoastal Research Group（地理海岸研究组）；Transitional Artificial Intelligence Research Group（过渡人工智能研究组）
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Jagiellonian University（雅盖隆大学）；IDEAS NCBR（IDEAS国家数字科学研究中心）；Eindhoven University of Technology（埃因霍温理工大学）；University of Luxembourg（卢森堡大学）；University of Twente（特温特大学）
We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned. Our work and contribution consist of four parts: (i) We find that, as the ReLU-based network is iteratively pruned, the distribution of weight gradients tends to become narrower. This leads to the finding that as the network becomes more sparse, a larger value of LR should be used to train the pruned network. (ii) Motivated by this finding, we propose a novel LR schedule, called S-Cyclical (S-Cyc) which adapts the conventional cyclical LR sc…
Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
School of Computer Science and Technology, University of Science and Technology of China, China（计算机科学与技术学院，中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China, China（苏州先进研究院，中国科学技术大学）
SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity
Northwestern Polytechnical University（西北工业大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）；Beijing Jiaotong University（北京交通大学）；Harbin Engineering University（哈尔滨工程大学）
