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
Published in:IEEE open journal of solid-state circuits 2022, Vol.2, p.231-243
Main Authors: Garofalo, Angelo, Tortorella, Yvan, Perotti, Matteo, Valente, Luca, Nadalini, Alessandro, Benini, Luca, Rossi, Davide, Conti, Francesco
Format: Article
Language:English
Subjects:
Acceleration
Accelerators
Artificial neural networks
Clustering methods
Clusters
Efficiency
Engines
Flexibility
Floating point arithmetic
Hardware
Heterogeneous cluster
Human computer interaction
Inference
Integers
Kernels
Low power electronics
Multiplication
System on chip
tensor product engine (TPE)
Tensors
Training
ultralow-power AI
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