118 citations · 187 across the 6 of their papers we have counts for
7 papers
PATRONoC: Parallel AXI Transport Reducing Overhead for Networks-on-Chip targeting Multi-Accelerator DNN Platforms at the Edge
Vikram Jain, Matheus Cavalcante, Nazareno Bruschi +6
Emerging deep neural network (DNN) applications require high-performance multi-core hardware acceleration with large data bursts. Classical network-on-chips (NoCs) use serial packe…
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 (…
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture
Nazareno Bruschi, Giuseppe Tagliavini, Angelo Garofalo +4
The demand for computation resources and energy efficiency of Convolutional Neural Networks (CNN) applications requires a new paradigm to overcome the "Memory Wall". Analog In-Memo…
Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
Nazareno Bruschi, Giuseppe Tagliavini, Francesco Conti +7
Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm for heterogeneous computing, potentially delivering orders of magnitude better peak performance and efficienc…
GVSoC: A Highly Configurable, Fast and Accurate Full-Platform Simulator for RISC-V based IoT Processors
Nazareno Bruschi, Germain Haugou, Giuseppe Tagliavini +3
The last few years have seen the emergence of IoT processors: ultra-low power systems-on-chips (SoCs) combining lightweight and flexible micro-controller units (MCUs), often based…
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…