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

Inexact computing aims to compute good solutions that require considerablyless resource -- typically energy -- compared to computing exact solutions.While inexactness is motivated by concerns derived from technology scaling andMoore's law, there is no formal or foundational framework for reasoning aboutthis novel approach to designing algorithms. In this work, we present afundamental relationship between the quality of computing the value of aboolean function and the energy needed to compute it in a mathematicallyrigorous and general setting. On this basis, one can study the tradeoff betweenthe quality of the solution to a problem and the amount of energy that isconsumed. We accomplish this by introducing a computational model to classifyproblems based on notions of symmetry inspired by physics. We show that someproblems are symmetric in that every input bit is, in a sense, equallyimportant, while other problems display a great deal of asymmetry in theimportance of input bits. We believe that our model is novel and provides afoundation for inexact Computing. Building on this, we show that asymmetricproblems allow us to invest resources favoring the important bits -- a featurethat can be leveraged to design efficient inexact algorithms. On the negativeside and in contrast, we can prove that the best inexact algorithms forsymmetric problems are no better than simply reducing the resource investmentuniformly across all bits. Akin to classical theories concerned with space andtime complexity, we believe the ability to classify problems as shown in ourpaper will serve as a basis for formally reasoning about the effectiveness ofinexactness in the context of a range of computational problems with energybeing the primary resource.

Sustaining Moore's Law Through Inexactness