13 citations · 21 across the 14 of their papers we have counts for
15 papers
Physically-Aware Preemptive Virtual Channels for Deadlock-Free AXI Networks-on-Chip
Lorenzo Leone, Luca Colagrande, Luca Benini
As many-core Systems-on-Chip (SoCs) continue to scale, Networks-on-Chip (NoCs) must sustain increasingly high memory bandwidth while preserving deadlock freedom. In AXI4 systems, p…
FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Large Attention-Based Model Inference on Tile-Based Accelerators
Chi Zhang, Luca Colagrande, Renzo Andri +1
Attention accounts for an increasingly dominant fraction of total computation during inference for mixture-of-experts (MoE) models, making efficient acceleration critical. Emerging…
A Lightweight High-Throughput Collective-Capable NoC for Large-Scale ML Accelerators
Luca Colagrande, Lorenzo Leone, Chen Wu +3
The exponential increase in Machine Learning (ML) model size and complexity has driven unprecedented demand for high-performance acceleration systems. As technology scaling enables…
Late Breaking Results: Boosting Efficient Dual-Issue Execution on Lightweight RISC-V Cores
Luca Colagrande, Luca Benini
Large-scale ML accelerators rely on large numbers of PEs, imposing strict bounds on the area and energy budget of each PE. Prior work demonstrates that limited dual-issue capabilit…
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…
Late Breaking Results: A RISC-V ISA Extension for Chaining in Scalar Processors
Luca Colagrande, Jayanth Jonnalagadda, Luca Benini
Modern general-purpose accelerators integrate a large number of programmable area- and energy-efficient processing elements (PEs), to deliver high performance while meeting stringe…
Dual-Issue Execution of Mixed Integer and Floating-Point Workloads on Energy-Efficient In-Order RISC-V Cores
Luca Colagrande, Luca Benini
To meet the computational requirements of modern workloads under tight energy constraints, general-purpose accelerator architectures have to integrate an ever-increasing number of…
A Multicast-Capable AXI Crossbar for Many-core Machine Learning Accelerators
Luca Colagrande, Luca Benini
To keep up with the growing computational requirements of machine learning workloads, many-core accelerators integrate an ever-increasing number of processing elements, putting the…
Occamy: A 432-Core Dual-Chiplet Dual-HBM2E 768-DP-GFLOP/s RISC-V System for 8-to-64-bit Dense and Sparse Computing in 12nm FinFET
Paul Scheffler, Thomas Benz, Viviane Potocnik +12
ML and HPC applications increasingly combine dense and sparse memory access computations to maximize storage efficiency. However, existing CPUs and GPUs struggle to flexibly handle…
Occamy: A 432-Core 28.1 DP-GFLOP/s/W 83% FPU Utilization Dual-Chiplet, Dual-HBM2E RISC-V-based Accelerator for Stencil and Sparse Linear Algebra Computations with 8-to-64-bit Floating-Point Support in 12nm FinFET
Gianna Paulin, Paul Scheffler, Thomas Benz +11
We present Occamy, a 432-core RISC-V dual-chiplet 2.5D system for efficient sparse linear algebra and stencil computations on FP64 and narrow (32-, 16-, 8-bit) SIMD FP data. Occamy…
Optimizing Foundation Model Inference on a Many-tiny-core Open-source RISC-V Platform
Viviane Potocnik, Luca Colagrande, Tim Fischer +4
Transformer-based foundation models have become crucial for various domains, most notably natural language processing (NLP) or computer vision (CV). These models are predominantly…
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…
Optimizing Offload Performance in Heterogeneous MPSoCs
Luca Colagrande, Luca Benini
Heterogeneous multi-core architectures combine a few "host" cores, optimized for single-thread performance, with many small energy-efficient "accelerator" cores for data-parallel p…