3 citations · 3 across the 4 of their papers we have counts for
4 papers
Towards Zero-Stall Matrix Multiplication on Energy-Efficient RISC-V Clusters for Machine Learning Acceleration
Luca Colagrande, Lorenzo Leone, Maximilian Coco +2
The growing computational demands of machine learning (ML) workloads have driven the design of ML accelerators aiming at an optimal tradeoff between efficiency and flexibility. A w…
FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Efficient Multi-Head Attention on Tile-Based Many-PE Accelerators
Chi Zhang, Luca Colagrande, Renzo Andri +6
Multi-Head Attention (MHA) is a critical computational kernel in transformer-based AI models. Emerging scalable tile-based accelerator architectures integrate increasing numbers of…
Taming Offload Overheads in a Massively Parallel Open-Source RISC-V MPSoC: Analysis and Optimization
Luca Colagrande, Luca Benini
Heterogeneous multi-core architectures combine on a single chip a few large, general-purpose host cores, optimized for single-thread performance, with (many) clusters of small, spe…
SARIS: Accelerating Stencil Computations on Energy-Efficient RISC-V Compute Clusters with Indirect Stream Registers
Paul Scheffler, Luca Colagrande, Luca Benini
Stencil codes are performance-critical in many compute-intensive applications, but suffer from significant address calculation and irregular memory access overheads. This work pres…