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Efficient acceleration of deep learning inference on resource-constrained edge devices: A review
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …
in breakthroughs in many areas. However, deploying these highly accurate models for data …
TinyML for ultra-low power AI and large scale IoT deployments: A systematic review
The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …
[HTML][HTML] An analog-AI chip for energy-efficient speech recognition and transcription
S Ambrogio, P Narayanan, A Okazaki, A Fasoli… - Nature, 2023 - nature.com
Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
Tensorflow lite micro: Embedded machine learning for tinyml systems
R David, J Duke, A Jain… - Proceedings of …, 2021 - proceedings.mlsys.org
Abstract We introduce TensorFlow (TF) Micro, an open-source machine learning inference
framework for running deep-learning models on embedded systems. TF Micro tackles the …
framework for running deep-learning models on embedded systems. TF Micro tackles the …
Tinyml meets iot: A comprehensive survey
L Dutta, S Bharali - Internet of Things, 2021 - Elsevier
The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …
Machine learning for microcontroller-class hardware: A review
SS Saha, SS Sandha, M Srivastava - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
Edge intelligence: Empowering intelligence to the edge of network
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …
caching, processing, and analysis proximity to where data are captured based on artificial …
Mlperf tiny benchmark
C Banbury, VJ Reddi, P Torelli, J Holleman… - ar** the training …