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 …
such as computer vision, speech recognition, and natural language processing. However …
AccelTran: A sparsity-aware accelerator for dynamic inference with transformers
Self-attention-based transformer models have achieved tremendous success in the domain
of natural language processing. Despite their efficacy, accelerating the transformer is …
of natural language processing. Despite their efficacy, accelerating the transformer is …
Hardware accelerator design for sparse dnn inference and training: A tutorial
Deep neural networks (DNNs) are widely used in many fields, such as artificial intelligence
generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning …
generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning …
EdgeTran: Device-aware co-search of transformers for efficient inference on mobile edge platforms
Automated design of efficient transformer models has recently attracted significant attention
from industry and academia. However, most works only focus on certain metrics while …
from industry and academia. However, most works only focus on certain metrics while …
EdgeTran: Co-designing transformers for efficient inference on mobile edge platforms
Automated design of efficient transformer models has recently attracted significant attention
from industry and academia. However, most works only focus on certain metrics while …
from industry and academia. However, most works only focus on certain metrics while …
TransCODE: Co-design of transformers and accelerators for efficient training and inference
Automated co-design of machine learning models and evaluation hardware is critical for
efficiently deploying such models at scale. Despite the state-of-the-art performance of …
efficiently deploying such models at scale. Despite the state-of-the-art performance of …
A review of bayesian methods in electronic design automation
The utilization of Bayesian methods has been widely acknowledged as a viable solution for
tackling various challenges in electronic integrated circuit (IC) design under stochastic …
tackling various challenges in electronic integrated circuit (IC) design under stochastic …
[HTML][HTML] A Coarse-and Fine-Grained Co-Exploration Approach for Optimizing DNN Spatial Accelerators: Improving Speed and Performance
H Sun, J Shen, C Zhang, H Liu - Electronics, 2025 - mdpi.com
The rapid advancement of deep neural networks has significantly increased demands for
computational complexity and data volume. This trend is especially evident with the …
computational complexity and data volume. This trend is especially evident with the …
Scratchpad Memory Management for Deep Learning Accelerators
The success of Artificial Intelligence (AI) applications is driven by efficient hardware
accelerators. Recent trends show a rapid increase in the application demands, which in …
accelerators. Recent trends show a rapid increase in the application demands, which in …
BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration
Researchers constantly strive to explore larger and more complex search spaces in various
scientific studies and physical experiments. However, such investigations often involve …
scientific studies and physical experiments. However, such investigations often involve …