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

Many neural networks exhibit stability in their activation patterns over timein response to inputs from sensors operating under real-world conditions. Bycapitalizing on this property of natural signals, we propose a Recurrent NeuralNetwork (RNN) architecture called a delta network in which each neurontransmits its value only when the change in its activation exceeds a threshold.The execution of RNNs as delta networks is attractive because their states mustbe stored and fetched at every timestep, unlike in convolutional neuralnetworks (CNNs). We show that a naive run-time delta network implementationoffers modest improvements on the number of memory accesses and computes, butoptimized training techniques confer higher accuracy at higher speedup. Withthese optimizations, we demonstrate a 9X reduction in cost with negligible lossof accuracy for the TIDIGITS audio digit recognition benchmark. Similarly, onthe large Wall Street Journal speech recognition benchmark even existingnetworks can be greatly accelerated as delta networks, and a 5.7x improvementwith negligible loss of accuracy can be obtained through training. Finally, onan end-to-end CNN trained for steering angle prediction in a driving dataset,the RNN cost can be reduced by a substantial 100X.

Delta Networks for Optimized Recurrent Network Computation