A survey of FPGA and ASIC designs for transformer inference acceleration and optimization

BJ Kang, HI Lee, SK Yoon, YC Kim, SB Jeong… - Journal of Systems …, 2024 - Elsevier
Recently, transformer-based models have achieved remarkable success in various fields,
such as computer vision, speech recognition, and natural language processing. However …

A Heterogeneous Chiplet Architecture for Accelerating End-to-End Transformer Models

H Sharma, P Dhingra, JR Doppa, U Ogras… - arXiv preprint arXiv …, 2023 - arxiv.org
Transformers have revolutionized deep learning and generative modeling, enabling
unprecedented advancements in natural language processing tasks. However, the size of …

EdgeTran: Device-aware co-search of transformers for efficient inference on mobile edge platforms

S Tuli, NK Jha - IEEE Transactions on Mobile Computing, 2023 - ieeexplore.ieee.org
Automated design of efficient transformer models has recently attracted significant attention
from industry and academia. However, most works only focus on certain metrics while …

Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design

X Yang, Z Wang, XS Hu, CH Kim, S Yu… - … on Circuits and …, 2023 - ieeexplore.ieee.org
The rapid progress of artificial intelligence (AI) has led to the emergence of a highly
promising field known as neuro-symbolic (NeSy) computing. This approach combines the …

Enhancing Deep Neural Networks in Diverse Resource-Constrained Hardware Settings

S Tuli - 2024 - search.proquest.com
Over the past decade, artificial intelligence (AI) has gained significant interest in industry and
academia. Deep neural network (DNN) models have exploded in size over the years. Wider …

A Survey: Hardware Neural Architecture Search On FPGA/ASIC

S Deng - 2024 - webthesis.biblio.polito.it
Deep learning (DL) systems are revolutionizing technology across various fields. These
breakthroughs are driven by the availability of big data, tremendous growth in computational …