Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities
In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor
data, an increasing number of actuators, and data-intensive algorithms, the development of …
data, an increasing number of actuators, and data-intensive algorithms, the development of …
Hardware/software co-design for tinyml voice-recognition application on resource frugal Edge Devices
On-device artificial intelligence has attracted attention globally, and attempts to combine the
internet of things and TinyML (machine learning) applications are increasing. Although most …
internet of things and TinyML (machine learning) applications are increasing. Although most …
Ultra-compact binary neural networks for human activity recognition on RISC-V processors
Human Activity Recognition (HAR) is a relevant inference task in many mobile applications.
State-of-the-art HAR at the edge is typically achieved with lightweight machine learning …
State-of-the-art HAR at the edge is typically achieved with lightweight machine learning …
An energy-efficient mixed-bit CNN accelerator with column parallel readout for ReRAM-based in-memory computing
D Liu, H Zhou, W Mao, J Liu, Y Han… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
Computing-In-memory (CIM) accelerators have the characteristics of storage and computing
integration, which has the potential to break through the limit of Moore's law and the …
integration, which has the potential to break through the limit of Moore's law and the …
Artificial intelligence accelerators
Artificial intelligence (AI) algorithms are extremely computational-intensive on voluminous
data. AI accelerators are desired to satisfy their hardware demands. This chapter introduces …
data. AI accelerators are desired to satisfy their hardware demands. This chapter introduces …
An energy-efficient mixed-bitwidth systolic accelerator for NAS-optimized deep neural networks
Optimized deep neural network (DNN) models and energy-efficient hardware designs are of
great importance in edge-computing applications. The neural architecture search (NAS) …
great importance in edge-computing applications. The neural architecture search (NAS) …
Best practices for the deployment of edge inference: The conclusions to start designing
G Flamis, S Kalapothas, P Kitsos - Electronics, 2021 - mdpi.com
The number of Artificial Intelligence (AI) and Machine Learning (ML) designs is rapidly
increasing and certain concerns are raised on how to start an AI design for edge systems …
increasing and certain concerns are raised on how to start an AI design for edge systems …
[图书][B] Artificial Intelligence and Hardware Accelerators
Artificial intelligence (AI) is designing new genesis around the globe and garnering great
attention from industries and academia. AI algorithms are indigenously intensely …
attention from industries and academia. AI algorithms are indigenously intensely …
RISC-V based Fully-Parallel SRAM Computing-in-Memory Accelerator with High Hardware Utilization and Data Reuse Rate
H Zhou, H Hong, D Liu, H Liu, Y Xia, K Li… - 2023 IEEE 5th …, 2023 - ieeexplore.ieee.org
Computing-In-memory (CIM) accelerators have the characteristics of storage and computing
integration, which can effectively improve the computing efficiency of the convolutional …
integration, which can effectively improve the computing efficiency of the convolutional …
High-speed bnn design in hls with optimized classification and computation method
D Lee, Y Kim - … on Consumer Electronics-Asia (ICCE-Asia), 2022 - ieeexplore.ieee.org
With the development of computer vision and artificial intelligence, many neural networks
are being studied. Also, FPGAs are being used in many fields in modern society by its …
are being studied. Also, FPGAs are being used in many fields in modern society by its …