Modern IoT end nodes must support computational intensive workloads at a limited power-budget. Parallel ultra-low-power architectures are a promising target for this scenario, and the availability of highly optimized software libraries is crucial to exploit parallelism and reduce software development costs. This letter proposes an efficient parallel design of the widely used STFT and DWT transforms targeting ultra-low-power IoT devices. We address key performance challenges related to fine-grained synchronization and banking conflicts in shared memory. We achieve high throughput (50.95 samples/μs, on average), good parallel speedup (up to 6.79×), and high energy efficiency (up to 172.55 GOp/s/W) on a cluster of 8 RISC-V cores optimized for parallel ultra-low-power (PULP) operation.

Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes / Mazzoni, B.; Benatti, S.; Benini, L.; Tagliavini, G.. - In: IEEE EMBEDDED SYSTEMS LETTERS. - ISSN 1943-0663. - 13:4(2021), pp. 210-213. [10.1109/LES.2021.3065206]

Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes

Benatti S.;
2021

Abstract

Modern IoT end nodes must support computational intensive workloads at a limited power-budget. Parallel ultra-low-power architectures are a promising target for this scenario, and the availability of highly optimized software libraries is crucial to exploit parallelism and reduce software development costs. This letter proposes an efficient parallel design of the widely used STFT and DWT transforms targeting ultra-low-power IoT devices. We address key performance challenges related to fine-grained synchronization and banking conflicts in shared memory. We achieve high throughput (50.95 samples/μs, on average), good parallel speedup (up to 6.79×), and high energy efficiency (up to 172.55 GOp/s/W) on a cluster of 8 RISC-V cores optimized for parallel ultra-low-power (PULP) operation.
2021
13
4
210
213
Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes / Mazzoni, B.; Benatti, S.; Benini, L.; Tagliavini, G.. - In: IEEE EMBEDDED SYSTEMS LETTERS. - ISSN 1943-0663. - 13:4(2021), pp. 210-213. [10.1109/LES.2021.3065206]
Mazzoni, B.; Benatti, S.; Benini, L.; Tagliavini, G.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1264935
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact