A Strategy to Maximize the Utilization of AI Neural Processors on an Automotive Computing Platform
K Sohn, I Choi, S Kim, J Lee, J Lee… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Advancements in AI are transforming the automotive industry, creating opportunities for AI-
powered software and hardware. AI-driven features in automobiles are increasingly …
powered software and hardware. AI-driven features in automobiles are increasingly …
Proai: An efficient embedded ai hardware for automotive applications-a benchmark study
S Mantowsky, F Heuer, S Bukhari… - Proceedings of the …, 2021 - openaccess.thecvf.com
Abstract Development in the field of Single Board Computers (SBC) have been increasing
for several years. They provide a good balance between computing performance and power …
for several years. They provide a good balance between computing performance and power …
Optimizing Edge AI Solutions through Hardware and Software Co-Design
This paper introduces Edge AI solutions powered by DEEPX's Neural Processing Units
(NPUs): DX-L1, L2, and M1. Our work focuses on two aspects:(1) the specialized NPU …
(NPUs): DX-L1, L2, and M1. Our work focuses on two aspects:(1) the specialized NPU …
ODMDEF: on-device multi-DNN execution framework utilizing adaptive layer-allocation on general purpose cores and accelerators
C Lim, M Kim - IEEE Access, 2021 - ieeexplore.ieee.org
On-device DNN processing has been common interests in the field of autonomous driving
research. For better accuracy, both the number of DNN models and the model-complexity …
research. For better accuracy, both the number of DNN models and the model-complexity …
MultiRuler: A Multi-Dimensional Resource Modeling Method for Embedded Intelligent Systems of Autonomous Driving
With the development of vehicular technology, autonomous driving has experienced
explosive growth, making it an important and popular research field. In autonomous driving …
explosive growth, making it an important and popular research field. In autonomous driving …
Research and Design of Neural Processing Architectures Optimized for Embedded Applications
B Wu - 2024 - tud.qucosa.de
Deploying neural networks on edge devices and bringing them into our daily lives is
attracting more and more attention. However, its expensive computational cost makes many …
attracting more and more attention. However, its expensive computational cost makes many …
Predictable DNN Inference for Autonomous Driving
L Liu - 2023 - search.proquest.com
Deep neural networks (DNNs) are widely used in autonomous driving due to their high
accuracy for perception, decision, and control. Predictability of the perception module is …
accuracy for perception, decision, and control. Predictability of the perception module is …
Predjoule: A timing-predictable energy optimization framework for deep neural networks
The revolution of deep neural networks (DNNs) is enabling dramatically better autonomy in
autonomous driving. However, it is not straightforward to simultaneously achieve both timing …
autonomous driving. However, it is not straightforward to simultaneously achieve both timing …
A close look at multi-tenant parallel CNN inference for autonomous driving
Convolutional neural networks (CNNs) are widely used in vision-based autonomous driving,
ie, detecting and localizing objects captured in live video streams. Although CNNs …
ie, detecting and localizing objects captured in live video streams. Although CNNs …
Samsung neural processing unit: An ai accelerator and sdk for flagship mobile ap
Samsung Neural Processing Unit : An AI accelerator and SDK for flagship mobile AP Page
1 Samsung Neural Processing Unit An AI accelerator and SDK for flagship mobile AP Jun-Seok …
1 Samsung Neural Processing Unit An AI accelerator and SDK for flagship mobile AP Jun-Seok …