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

Neuromorphic computers perform computations by emulating the human brain, anduse extremely low power. They are expected to be indispensable forenergy-efficient computing in the future. While they are primarily used inspiking neural network-based machine learning applications, neuromorphiccomputers are known to be Turing-complete, and thus, capable of general-purposecomputation. However, to fully realize their potential for general-purpose,energy-efficient computing, it is important to devise efficient mechanisms forencoding numbers. Current encoding approaches have limited applicability andmay not be suitable for general-purpose computation. In this paper, we presentthe virtual neuron as an encoding mechanism for integers and rational numbers.We evaluate the performance of the virtual neuron on physical and simulatedneuromorphic hardware and show that it can perform an addition operation using23 nJ of energy on average using a mixed-signal memristor-based neuromorphicprocessor. We also demonstrate its utility by using it in some of themu-recursive functions, which are the building blocks of general-purposecomputation.

Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron