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LoTTA: Energy-Efficient Processor for Always-on Applications

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2018 IEEE International Workshop on Signal Processing Systems (SiPS)
PublisherIEEE
ISBN (Electronic)978-1-5386-6318-9
ISBN (Print)978-1-5386-6319-6
DOIs
Publication statusPublished - Oct 2018
Publication typeA4 Article in a conference publication
EventIEEE International Workshop on Signal Processing Systems -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2374-7390

Conference

ConferenceIEEE International Workshop on Signal Processing Systems
Period1/01/00 → …

Abstract

Various use cases in the era of Internet-of-Things (IoT) demand processor devices to have low energy consumption in order to maximize the battery life. In addition to energy constraints, there is often a need to both swiftly execute control-oriented code to provide low reaction times and to occasionally perform real time signal processing tasks efficiently. As a response to these requirements, we propose LoTTA, an extremely energy-efficient exposed datapath core. Its transport-triggered programming model helps in lowering the execution latency via low cost data forwarding. Control efficiency is achieved by an optimized control unit with zero delay slot branches and predicated execution. An instruction register file is included for frequently executed program hot spots to reduce the instruction stream energy consumption. These features allow the processor to execute CHStone and EEMBC CoreMark benchmarks on average with 19% fewer cycles compared to a 6-stage LM32, a traditional RISC core with similar datapath resources. The core consumes 53% less energy on average compared to the RISC core. When including the instruction stream overheads, in the best case, LoTTA saves 79% energy, and on average 40%.

Keywords

  • Energy efficiency, low power, Computer architecture, IoT, always-on, Processor

Publication forum classification

Field of science, Statistics Finland