355 citations · 980 across the 44 of their papers we have counts for
5 papers · 1 filter
Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication
Jannis Schönleber, Lukas Cavigelli, Matteo Perotti +2
Artificial intelligence has surged in recent years, with advancements in machine learning rapidly impacting nearly every area of life. However, the growing complexity of these mode…
Ara2: Exploring Single- and Multi-Core Vector Processing with an Efficient RVV 1.0 Compliant Open-Source Processor
Matteo Perotti, Matheus Cavalcante, Renzo Andri +2
Vector processing is highly effective in boosting processor performance and efficiency for data-parallel workloads. In this paper, we present Ara2, the first fully open-source vect…
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
Antoine Scardigli, Lukas Cavigelli, Lorenz K. Müller
Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time application…
ReDSEa: Automated Acceleration of Triangular Solver on Supercloud Heterogeneous Systems
Georgios Zacharopoulos, Ilias Bournias, Verner Vlacic +1
When utilized effectively, Supercloud heterogeneous systems have the potential to significantly enhance performance. Our ReDSEa tool-chain automates the mapping, load balancing, sc…
Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation
Enrico Reggiani, Renzo Andri, Lukas Cavigelli
Modern DNN workloads increasingly rely on activation functions consisting of computationally complex operations. This poses a challenge to current accelerators optimized for convol…