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

We present a 3.1 POp/s/W fully digital hardware accelerator for ternaryneural networks. CUTIE, the Completely Unrolled Ternary Inference Engine,focuses on minimizing non-computational energy and switching activity so thatdynamic power spent on storing (locally or globally) intermediate results isminimized. This is achieved by 1) a data path architecture completely unrolledin the feature map and filter dimensions to reduce switching activity byfavoring silencing over iterative computation and maximizing data re-use, 2)targeting ternary neural networks which, in contrast to binary NNs, allow forsparse weights which reduce switching activity, and 3) introducing an optimizedtraining method for higher sparsity of the filter weights, resulting in afurther reduction of the switching activity. Compared with state-of-the-artaccelerators, CUTIE achieves greater or equal accuracy while decreasing theoverall core inference energy cost by a factor of 4.8x-21x.

CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency