English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Resource is an important constraint when deploying Deep Neural Networks(DNNs) on mobile and edge devices. Existing works commonly adopt the cell-basedsearch approach, which limits the flexibility of network patterns in learnedcell structures. Moreover, due to the topology-agnostic nature of existingworks, including both cell-based and node-based approaches, the search processis time consuming and the performance of found architecture may be sub-optimal.To address these problems, we propose AutoShrink, a topology-aware NeuralArchitecture Search(NAS) for searching efficient building blocks of neuralarchitectures. Our method is node-based and thus can learn flexible networkpatterns in cell structures within a topological search space. Directed AcyclicGraphs (DAGs) are used to abstract DNN architectures and progressively optimizethe cell structure through edge shrinking. As the search space intrinsicallyreduces as the edges are progressively shrunk, AutoShrink explores moreflexible search space with even less search time. We evaluate AutoShrink onimage classification and language tasks by crafting ShrinkCNN and ShrinkRNNmodels. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34%Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy ofstate-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN arecrafted within 1.5 GPU hours, which is 7.2x and 6.7x faster than the craftingtime of SOTA CNN and RNN models, respectively.

AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture