19 citations · 38 across the 6 of their papers we have counts for
6 papers
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…
Error Checking for Sparse Systolic Tensor Arrays
Christodoulos Peltekis, Dionysios Filippas, Giorgos Dimitrakopoulos
Structured sparsity is an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. In such cases,…
The Case for Asymmetric Systolic Array Floorplanning
C. Peltekis, D. Filippas, G. Dimitrakopoulos +1
The widespread proliferation of deep learning applications has triggered the need to accelerate them directly in hardware. General Matrix Multiplication (GEMM) kernels are elementa…
Low-Power Data Streaming in Systolic Arrays with Bus-Invert Coding and Zero-Value Clock Gating
C. Peltekis, D. Filippas, G. Dimitrakopoulos +1
Systolic Array (SA) architectures are well suited for accelerating matrix multiplications through the use of a pipelined array of Processing Elements (PEs) communicating with local…
Reduced-Precision Floating-Point Arithmetic in Systolic Arrays with Skewed Pipelines
D. Filippas, C. Peltekis, G. Dimitrakopoulos +1
The acceleration of deep-learning kernels in hardware relies on matrix multiplications that are executed efficiently on Systolic Arrays (SA). To effectively trade off deep-learning…
ArrayFlex: A Systolic Array Architecture with Configurable Transparent Pipelining
C. Peltekis, D. Filippas, G. Dimitrakopoulos +2
Convolutional Neural Networks (CNNs) are the state-of-the-art solution for many deep learning applications. For maximum scalability, their computation should combine high performan…