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

Recurrent neural networks (RNNs) are state-of-the-art in voiceawareness/understanding and speech recognition. On-device computation of RNNson low-power mobile and wearable devices would be key to applications such aszero-latency voice-based human-machine interfaces. Here we present Chipmunk, asmall (<1 mm${}^2$) hardware accelerator for Long-Short Term Memory RNNs in UMC65 nm technology capable to operate at a measured peak efficiency up to 3.08Gop/s/mW at 1.24 mW peak power. To implement big RNN models without incurringin huge memory transfer overhead, multiple Chipmunk engines can cooperate toform a single systolic array. In this way, the Chipmunk architecture in a 75tiles configuration can achieve real-time phoneme extraction on a demanding RNNtopology proposed by Graves et al., consuming less than 13 mW of average power.

Chipmunk: A Systolically Scalable 0.9 mm${}^2$, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference