118 citations · 388 across the 21 of their papers we have counts for
8 papers · 1 filter
Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers
Emanuele Parisi, Francesco Barchi, Andrea Bartolini +2
The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit moder…
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes
Angelo Garofalo, Giuseppe Tagliavini, Francesco Conti +2
This work introduces lightweight extensions to the RISC-V ISA to boost the efficiency of heavily Quantized Neural Network (QNN) inference on microcontroller-class cores. By extendi…
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…
A transprecision floating-point cluster for efficient near-sensor data analytics
Fabio Montagna, Stefan Mach, Simone Benatti +5
Recent applications in the domain of near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this…
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
Alessio Burrello, Angelo Garofalo, Nazareno Bruschi +3
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applicati…
Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices
Nazareno Bruschi, Angelo Garofalo, Francesco Conti +2
The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions of modern instructi…
Energy-Efficient Hardware-Accelerated Synchronization for Shared-L1-Memory Multiprocessor Clusters
Florian Glaser, Giuseppe Tagliavini, Davide Rossi +3
The steeply growing performance demands for highly power- and energy-constrained processing systems such as end-nodes of the internet-of-things (IoT) have led to parallel near-thre…
Combining Learning and Optimization for Transprecision Computing
Andrea Borghesi, Giuseppe Tagliavini, Michele Lombardi +2
The growing demands of the worldwide IT infrastructure stress the need for reduced power consumption, which is addressed in so-called transprecision computing by improving energy e…