papers
Publications (2)
cs.AR2025
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…
cs.AR2025
VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers
Run Wang, Gamze Islamoglu, Andrea Belano +4
While Transformers are dominated by Floating-Point (FP) Matrix-Multiplications, their aggressive acceleration through dedicated hardware or many-core programmable systems has shift…