1 citations · 1 across the 12 of their papers we have counts for
12 papers
MIVE: A Minimalist Integer Vector Engine for Softmax LayerNorm and RMSNorm Acceleration
Kosmas Alexandridis, Giorgos Dimitrakopoulos
The rapid growth of Large Language Models (LLMs) has intensified the need for specialized hardware accelerators that can satisfy stringent inference latency and power constraints.…
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…
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…
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…
Online Alignment and Addition in Multi-Term Floating-Point Adders
Kosmas Alexandridis, Giorgos Dimitrakopoulos
Multi-term floating-point addition appears in vector dot-product computations, matrix multiplications, and other forms of floating-point data aggregation. A critical step in multi-…
Floating-Point Multiply-Add with Approximate Normalization for Low-Cost Matrix Engines
Kosmas Alexandridis, Christodoulos Peltekis, Dionysios Filippas +1
The widespread adoption of machine learning algorithms necessitates hardware acceleration to ensure efficient performance. This acceleration relies on custom matrix engines that op…
Reusing Softmax Hardware Unit for GELU Computation in Transformers
Christodoulos Peltekis, Kosmas Alexandridis, Giorgos Dimitrakopoulos
Transformers have improved drastically the performance of natural language processing (NLP) and computer vision applications. The computation of transformers involves matrix multip…
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…