190 citations · 1.5k across the 53 of their papers we have counts for
11 papers · 1 filter
Siracusa: A 16 nm Heterogenous RISC-V SoC for Extended Reality with At-MRAM Neural Engine
Arpan Suravi Prasad, Moritz Scherer, Francesco Conti +9
Extended reality (XR) applications are Machine Learning (ML)-intensive, featuring deep neural networks (DNNs) with millions of weights, tightly latency-bound (10-20 ms end-to-end),…
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge
Georg Rutishauser, Francesco Conti, Luca Benini
Mixed-precision quantization, where a deep neural network's layers are quantized to different precisions, offers the opportunity to optimize the trade-offs between model size, late…
A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
Alessandro Nadalini, Georg Rutishauser, Alessio Burrello +5
The emerging trend of deploying complex algorithms, such as Deep Neural Networks (DNNs), increasingly poses strict memory and energy efficiency requirements on Internet-of-Things (…
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi +19
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vi…
Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers
Davide Nadalini, Manuele Rusci, Luca Benini +1
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models…
Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays
Mattia Sinigaglia, Luca Bertaccini, Luca Valente +5
Emerging applications in the IoT domain require ultra-low-power and high-performance end-nodes to deal with complex near-sensor-data analytics. Domains such as audio, radar, and St…
Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing
Francesco Conti, Gianna Paulin, Angelo Garofalo +7
Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and nano-robotics need to run many diver…
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
Angelo Garofalo, Yvan Tortorella, Matteo Perotti +5
On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solut…
Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space
Michael Rogenmoser, Yvan Tortorella, Davide Rossi +2
Space Cyber-Physical Systems (S-CPS) such as spacecraft and satellites strongly rely on the reliability of onboard computers to guarantee the success of their missions. Relying sol…
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…
RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration
Yvan Tortorella, Luca Bertaccini, Luca Benini +2
The increasing interest in TinyML, i.e., near-sensor machine learning on power budgets of a few tens of mW, is currently pushing toward enabling TinyML-class training as opposed to…