Jun 6, 2022 · A novel hardware accelerator that improves neuron vector similarity detection and reduces the energy consumption of reuse-centric CNN inference.
This article presents an in-depth exploration of architectural support for reuse-centric CNN. It addresses some major limitations of the state-of-the-art design ...
This paper presents an in-depth exploration of architectural support for reuse-centric CNN. It addresses some major limitations of the state-of-the-art design, ...
Ozturk, “Energy efficient boosting of gemm accelerators for dnn via reuse,” ACM Trans. Des. Autom. Electron. Syst., dec 2021, just Accepted. [Online] ...
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 ...
Missing: DNN | Show results with:DNN
However, it may be necessary to design a domain-specific accelerator for handling the increased computation cost and improving energy efficiency, by taking ...
LRADNN: High-Throughput and Energy-Efficient Deep Neural Network Accelerator using Low Rank Approximation. (Hong Kong University of Science and Technology ...
These custom architectures offer better performance and energy efficiency concerning CPUs/GPUs thanks to an optimized data flow (or data reuse pattern) that ...
A new CNN accelerator called the Unique Weight CNN Accelerator (UCNN) is proposed, which uses weight repetition to reuse CNN sub-computations and to reduce CNN ...
Abstract—Convolutional neural networks (CNNs) are at the core of many state-of-the-art deep learning models in computer vision, speech, and text processing.