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

Tartan (TRT), a hardware accelerator for inference with Deep Neural Networks(DNNs), is presented and evaluated on Convolutional Neural Networks. TRTexploits the variable per layer precision requirements of DNNs to deliverexecution time that is proportional to the precision p in bits used per layerfor convolutional and fully-connected layers. Prior art has demonstrated anaccelerator with the same execution performance only for convolutional layers.Experiments on image classification CNNs show that on average across allnetworks studied, TRT outperforms a state-of-the-art bit-parallel acceleratorby 1:90x without any loss in accuracy while it is 1:17x more energy efficient.TRT requires no network retraining while it enables trading off accuracy foradditional improvements in execution performance and energy efficiency. Forexample, if a 1% relative loss in accuracy is acceptable, TRT is on average2:04x faster and 1:25x more energy efficient than a conventional bit-parallelaccelerator. A Tartan configuration that processes 2-bits at time, requiresless area than the 1-bit configuration, improves efficiency to 1:24x over thebit-parallel baseline while being 73% faster for convolutional layers and 60%faster for fully-connected layers is also presented.

Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability