Angelo Garofalo | DeepAI

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∙ 05/12/2023

Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays

Emerging applications in the IoT domain require ultra-low-power and high...

0 Mattia Sinigaglia, et al. ∙

∙ 03/31/2023

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

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose s...

0 Angelo Garofalo, et al. ∙

∙ 03/29/2023

Cyber Security aboard Micro Aerial Vehicles: An OpenTitan-based Visual Communication Use Case

Autonomous Micro Aerial Vehicles (MAVs), with a form factor of 10cm in d...

0 Maicol Ciani, et al. ∙

∙ 11/23/2022

End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture

The demand for computation resources and energy efficiency of Convolutio...

0 Nazareno Bruschi, et al. ∙

∙ 01/21/2022

Dustin: A 16-Cores Parallel Ultra-Low-Power Cluster with 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode

Computationally intensive algorithms such as Deep Neural Networks (DNNs)...

0 Gianmarco Ottavi, et al. ∙

∙ 01/04/2022

A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks

Deployment of modern TinyML tasks on small battery-constrained IoT devic...

3 Angelo Garofalo, et al. ∙

∙ 11/29/2020

XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes

This work introduces lightweight extensions to the RISC-V ISA to boost t...

0 Angelo Garofalo, et al. ∙

∙ 10/08/2020

A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference

Low bit-width Quantized Neural Networks (QNNs) enable deployment of comp...

0 Gianmarco Ottavi, et al. ∙

∙ 08/27/2020

A transprecision floating-point cluster for efficient near-sensor data analytics

Recent applications in the domain of near-sensor computing require the a...

0 Fabio Montagna, et al. ∙

∙ 08/17/2020

DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

The deployment of Deep Neural Networks (DNNs) on end-nodes at the extrem...

13 Alessio Burrello, et al. ∙

∙ 07/15/2020

Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices

The deployment of Quantized Neural Networks (QNN) on advanced microcontr...

0 Nazareno Bruschi, et al. ∙

∙ 08/29/2019

PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors

We present PULP-NN, an optimized computing library for a parallel ultra-...

0 Angelo Garofalo, et al. ∙