428 citations · 4.5k across the 191 of their papers we have counts for
22 papers · 1 filter
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces
Xiaying Wang, Lukas Cavigelli, Tibor Schneider +1
Motor imagery brain--machine interfaces enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the cla…
MemPool-3D: Boosting Performance and Efficiency of Shared-L1 Memory Many-Core Clusters with 3D Integration
Matheus Cavalcante, Anthony Agnesina, Samuel Riedel +6
Three-dimensional integrated circuits promise power, performance, and footprint gains compared to their 2D counterparts, thanks to drastic reductions in the interconnects' length t…
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays
Leonardo Ravaglia, Manuele Rusci, Davide Nadalini +3
In the last few years, research and development on Deep Learning models and techniques for ultra-low-power devices in a word, TinyML has mainly focused on a train-then-deploy assum…
Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode
Davide Rossi, Francesco Conti, Manuel Eggimann +9
The Internet-of-Things requires end-nodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibi…
A Fully-Integrated 5mW, 0.8Gbps Energy-Efficient Chip-to-Chip Data Link for Ultra-Low-Power IoT End-Nodes in 65-nm CMOS
Hayate Okuhara, Ahmed Elnaqib, Martino Dazzi +4
The increasing complexity of Internet-of-Things (IoT) applications and near-sensor processing algorithms is pushing the computational power of low-power, battery-operated end-node…
End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?
Gianmarco Ottavi, Geethan Karunaratne, Francesco Conti +3
In-Memory Acceleration (IMA) promises major efficiency improvements in deep neural network (DNN) inference, but challenges remain in the integration of IMA within a digital system.…