34 citations · 34 across the 3 of their papers we have counts for
3 papers
Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers
Davide Nadalini, Manuele Rusci, Luca Benini +1
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models…
Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays
Mattia Sinigaglia, Luca Bertaccini, Luca Valente +5
Emerging applications in the IoT domain require ultra-low-power and high-performance end-nodes to deal with complex near-sensor-data analytics. Domains such as audio, radar, and St…
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
Angelo Garofalo, Yvan Tortorella, Matteo Perotti +5
On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solut…