48 citations · 154 across the 27 of their papers we have counts for
12 papers · 1 filter
MAUPITI: On-Device Prototype-Based Learning on a Smart Infrared Sensor
Beatrice Alessandra Motetti, Tanguy Dugas du Villard, Matteo Risso +7
Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pix…
Don't be so Stief! Learning KV Cache low-rank approximation over the Stiefel manifold
Luca Benfenati, Matteo Risso, Andrea Vannozzi +5
Key-value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capacity and bandwidth. A common mitig…
Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time
Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari
The demand for executing Deep Neural Networks (DNNs) with low latency and minimal power consumption at the edge has led to the development of advanced heterogeneous Systems-on-Chip…
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
Beatrice Alessandra Motetti, Matteo Risso, Alessio Burrello +3
The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mi…
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays
Matteo Risso, Chen Xie, Francesco Daghero +6
Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows while preserving privacy and minimizing e…
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices
Alessio Burrello, Matteo Risso, Beatrice Alessandra Motetti +3
The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can r…
PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimization
Daniele Jahier Pagliari, Matteo Risso, Beatrice Alessandra Motetti +1
Accurate yet efficient Deep Neural Networks (DNNs) are in high demand, especially for applications that require their execution on constrained edge devices. Finding such DNNs in a…
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference
Matteo Risso, Alessio Burrello, Giuseppe Maria Sarda +5
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a di…
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge
Matteo Risso, Alessio Burrello, Francesco Conti +6
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Obj…
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes
Matteo Risso, Alessio Burrello, Luca Benini +3
Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precis…
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks
Matteo Risso, Alessio Burrello, Luca Benini +3
Neural Architecture Search (NAS) is increasingly popular to automatically explore the accuracy versus computational complexity trade-off of Deep Learning (DL) architectures. When t…
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari +5
Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization…