4 citations · 5 across the 9 of their papers we have counts for
9 papers
Vectorized FlashAttention with Low-cost Exponential Computation in RISC-V Vector Processors
Vasileios Titopoulos, Kosmas Alexandridis, Giorgos Dimitrakopoulos
Attention is a core operation in numerous machine learning and artificial intelligence models. This work focuses on the acceleration of attention kernel using FlashAttention algori…
Custom Algorithm-based Fault Tolerance for Attention Layers in Transformers
Vasileios Titopoulos, Kosmas Alexandridis, Giorgos Dimitrakopoulos
Transformers and large language models (LLMs), powered by the attention mechanism, have transformed numerous AI applications, driving the need for specialized hardware accelerators…
Low-Cost FlashAttention with Fused Exponential and Multiplication Hardware Operators
Kosmas Alexandridis, Vasileios Titopoulos, Giorgos Dimitrakopoulos
Attention mechanisms, particularly within Transformer architectures and large language models (LLMs), have revolutionized sequence modeling in machine learning and artificial intel…
Efficient Implementation of RISC-V Vector Permutation Instructions
Vasileios Titopoulos, George Alexakis, Chrysostomos Nicopoulos +1
RISC-V CPUs leverage the RVV (RISC-V Vector) extension to accelerate data-parallel workloads. In addition to arithmetic operations, RVV includes powerful permutation instructions t…
FLASH-D: FlashAttention with Hidden Softmax Division
Kosmas Alexandridis, Vasileios Titopoulos, Giorgos Dimitrakopoulos
The transformer's attention mechanism has revolutionized AI and machine learning, with its efficient computation being crucial to its performance. However, calculating attention in…
Register Dispersion: Reducing the Footprint of the Vector Register File in Vector Engines of Low-Cost RISC-V CPUs
Vasileios Titopoulos, George Alexakis, Kosmas Alexandridis +2
The deployment of Machine Learning (ML) applications at the edge on resource-constrained devices has accentuated the need for efficient ML processing on low-cost processors. While…
Optimizing Structured-Sparse Matrix Multiplication in RISC-V Vector Processors
Vasileios Titopoulos, Kosmas Alexandridis, Christodoulos Peltekis +2
Structured sparsity has been proposed as an efficient way to prune the complexity of Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. Acc…
DeMM: A Decoupled Matrix Multiplication Engine Supporting Relaxed Structured Sparsity
Christodoulos Peltekis, Vasileios Titopoulos, Chrysostomos Nicopoulos +1
Deep Learning (DL) has achieved unprecedented success in various application domains. Meanwhile, model pruning has emerged as a viable solution to reduce the footprint of DL models…
IndexMAC: A Custom RISC-V Vector Instruction to Accelerate Structured-Sparse Matrix Multiplications
V. Titopoulos, K. Alexandridis, C. Peltekis +2
Structured sparsity has been proposed as an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardwa…