19 citations · 73 across the 28 of their papers we have counts for
10 papers · 1 filter
H-FA: A Hybrid Floating-Point and Logarithmic Approach to Hardware Accelerated FlashAttention
Kosmas Alexandridis, Giorgos Dimitrakopoulos
Transformers have significantly advanced AI and machine learning through their powerful attention mechanism. However, computing attention on long sequences can become a computation…
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…
High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos
The Number Theoretic Transform (NTT) is a fundamental operation in privacy-preserving technologies, particularly within fully homomorphic encryption (FHE). The efficiency of NTT co…
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…
Periodic Online Testing for Sparse Systolic Tensor Arrays
Christodoulos Peltekis, Chrysostomos Nicopoulos, Giorgos Dimitrakopoulos
Modern Machine Learning (ML) applications often benefit from structured sparsity, a technique that efficiently reduces model complexity and simplifies handling of sparse data in ha…
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…