Biological beings are intrinsically resilient. This means that they are able to continue to perform a task even if they are partially damaged or if some parts of them don’t work as expected. This is true also for the human brain. The research in these last years, however, has been concentrated on Artificial Intelligence (AI), to try to emulate the capabilities of the brain to improve itself, learn- ing from experience. Artificial Resilience (AR) is something not explored in detail yet. This four pages abstract present a Ph.D. path dedicated to the extensive study of Artificial Resilience in all its aspects. The study will target neuromorphic systems, in particu- lar Spiking Neural Networks, an emerging type of neural network models that try to mimic the behavior of a biological brain in a faithful way. In addition to this they are in general more suitable for an hardware acceleration. The goal of the Ph.D. is to realize a com- plete neuromorphic accelerator, configurable and resilient, and to apply it to improve the resilience of other electronic systems. Such an accelerator will be able to target area- and power-constrained applications in mission-critical environments, providing a more efficient alternative to classical techniques like Error Correction Codes (ECC) or redundancy to improve the robustness of a complex electronic system.

Artificial Resilience in neuromorphic systems / Carpegna, Alessio; Di Carlo, Stefano; Savino, Alessandro. - ELETTRONICO. - (2022), pp. 1-3. ((Intervento presentato al convegno International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies (HEART) 2022 tenutosi a Tsukuba, Japan nel June 9-10, 2022 [10.1145/3535044.3535062].

Artificial Resilience in neuromorphic systems

Carpegna, Alessio;Di Carlo, Stefano;Savino, Alessandro
2022

Abstract

Biological beings are intrinsically resilient. This means that they are able to continue to perform a task even if they are partially damaged or if some parts of them don’t work as expected. This is true also for the human brain. The research in these last years, however, has been concentrated on Artificial Intelligence (AI), to try to emulate the capabilities of the brain to improve itself, learn- ing from experience. Artificial Resilience (AR) is something not explored in detail yet. This four pages abstract present a Ph.D. path dedicated to the extensive study of Artificial Resilience in all its aspects. The study will target neuromorphic systems, in particu- lar Spiking Neural Networks, an emerging type of neural network models that try to mimic the behavior of a biological brain in a faithful way. In addition to this they are in general more suitable for an hardware acceleration. The goal of the Ph.D. is to realize a com- plete neuromorphic accelerator, configurable and resilient, and to apply it to improve the resilience of other electronic systems. Such an accelerator will be able to target area- and power-constrained applications in mission-critical environments, providing a more efficient alternative to classical techniques like Error Correction Codes (ECC) or redundancy to improve the robustness of a complex electronic system.
978-1-4503-9660-8
File in questo prodotto:
File Dimensione Formato  
phDworkInProgress.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 393.56 kB
Formato Adobe PDF
393.56 kB Adobe PDF Visualizza/Apri
heart2022-18.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 404.31 kB
Formato Adobe PDF
404.31 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2962854