DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

On-chip deep neural network (DNN) inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy, and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced cores with the performance...

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Bibliographic Details
Main Authors: Angelo Garofalo, Yvan Tortorella, Matteo Perotti, Luca Valente, Alessandro Nadalini, Luca Benini, Davide Rossi, Francesco Conti
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Open Journal of the Solid-State Circuits Society
Subjects:
Heterogeneous cluster
tensor product engine (TPE)
ultralow-power AI
Online Access:https://ieeexplore.ieee.org/document/9903915/

Internet

https://ieeexplore.ieee.org/document/9903915/

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