16 citations · 58 across the 10 of their papers we have counts for
5 papers · 1 filter
How to keep pushing ML accelerator performance? Know your rooflines!
Marian Verhelst, Luca Benini, Naveen Verma
The rapidly growing importance of Machine Learning (ML) applications, coupled with their ever-increasing model size and inference energy footprint, has created a strong need for sp…
FlooNoC: A 645 Gbps/link 0.15 pJ/B/hop Open-Source NoC with Wide Physical Links and End-to-End AXI4 Parallel Multi-Stream Support
Tim Fischer, Michael Rogenmoser, Thomas Benz +2
The new generation of domain-specific AI accelerators is characterized by rapidly increasing demands for bulk data transfers, as opposed to small, latency-critical cache line trans…
A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation
Luca Valente, Alessandro Nadalini, Asif Veeran +12
The rapid advancement of energy-efficient parallel ultra-low-power (ULP) ucontrollers units (MCUs) is enabling the development of autonomous nano-sized unmanned aerial vehicles (na…
TOP: Towards Open & Predictable Heterogeneous SoCs
Luca Valente, Francesco Restuccia, Davide Rossi +2
Ensuring predictability in modern real-time Systems-on-Chip (SoCs) is an increasingly critical concern for many application domains such as automotive, robotics, and industrial aut…
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),…