2023/06/27 by Jordi Fornt, Pau Fontova-Musté, Martí Caro +4 · 4 citations
Computer Science · Mathematics · #Advanced Neural Network Applications #Parallel Computing and Optimization Techniques #Tensor decomposition and applications
paper · doi:10.1109/tvlsi.2023.3286122
openalex publication_date 2023/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
Systolic array architectures have recently emerged as successful accelerators for deep convolutional neural network (CNN) inference. Such architectures can be used to efficiently execute general matrix–matrix multiplications (GeMMs), but computing convolutions with this primitive involves transforming the 3-D input tensor into an equivalent matrix, which can lead to an inflation of the input data, increasing the OFF-chip memory traffic which is critical for energy efficiency. In this work, we propose a GeMM-based systolic array accelerator that uses a novel data feeder architecture to perform ON-chip, on-the-fly convolution lowering (also known as im2col), supporting arbitrary tensor and kernel sizes as well as strided and dilated (or atrous) convolutions. By using our data feeder, we reduce memory transactions and required bandwidth on state-of-the-art CNNs by a factor of two, while only adding an area and power overhead of 4% and 7%, respectively. Application specific integrated circuit (ASIC) implementation of our accelerator in 22-nm technology fits in less than 1.1 mm 2 and reaches an energy efficiency of 1.10 TFLOP/sW with 16-bit floating-point arithmetic.