5 citations · 7 across the 2 of their papers we have counts for
3 papers
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
Angelo Garofalo, Gianmarco Ottavi, Francesco Conti +4
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory…
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.…
A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference
Gianmarco Ottavi, Angelo Garofalo, Giuseppe Tagliavini +3
Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine learning models on constrained devices such as microcontrollers (MCUs) by reducing their memory…