Radiation-Hardened-By-Design (RHBD) FPGAs have gained a lot of attention thanks to their excellent compromise between costs and performance. Being of very limited use due to a lack of performance a few years ago, these devices are now capable of implementing a wide range of applications requiring high computational capabilities. This work describes an implementation of a Very Long Instruction Word (VLIW) soft-core convolutional accelerator in the NanoXplore RHBD NG-Medium chip. Feasibility and timing performances have been analyzed in order to discover whether and how multi-core solutions can affect parallel acceleration. Placement also showed to heavily affect the delays, up to 70% more, based on the proximity to the output buffers.
Design Techniques for Multi-Core Neural Network Accelerators on Radiation-Hardened FPGAs / Portaluri, Andrea; Azimi, Sarah; Sterpone, Luca. - ELETTRONICO. - (2023), pp. 16-22. (Intervento presentato al convegno IEEE International Symposium on Parallel and Distributed Computing tenutosi a Bucharest (Romania) nel 10-12 July 2023) [10.1109/ISPDC59212.2023.00023].
Design Techniques for Multi-Core Neural Network Accelerators on Radiation-Hardened FPGAs
Andrea Portaluri;Sarah Azimi;Luca Sterpone
2023
Abstract
Radiation-Hardened-By-Design (RHBD) FPGAs have gained a lot of attention thanks to their excellent compromise between costs and performance. Being of very limited use due to a lack of performance a few years ago, these devices are now capable of implementing a wide range of applications requiring high computational capabilities. This work describes an implementation of a Very Long Instruction Word (VLIW) soft-core convolutional accelerator in the NanoXplore RHBD NG-Medium chip. Feasibility and timing performances have been analyzed in order to discover whether and how multi-core solutions can affect parallel acceleration. Placement also showed to heavily affect the delays, up to 70% more, based on the proximity to the output buffers.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2979316