#PockEngine

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#deep-learning models
MIT News | Massachusetts Institute of Technology
10 months ago
Artificial intelligence

Technique enables AI on edge devices to keep learning over time

Personalized deep-learning models can be trained on edge devices, eliminating the need to transmit sensitive user data to cloud servers.
PockEngine, a new on-device training method, enables fine-tuning of machine learning models directly on edge devices, improving privacy and reducing computational overhead.
PockEngine significantly speeds up on-device training, performing up to 15 times faster on some hardware platforms, without compromising accuracy. [ more ]
MIT News | Massachusetts Institute of Technology
10 months ago
Artificial intelligence

Technique enables AI on edge devices to keep learning over time

Personalized deep-learning models can be trained on edge devices, eliminating the need to transmit sensitive user data to cloud servers.
PockEngine, a new on-device training method, enables fine-tuning of machine learning models directly on edge devices, improving privacy and reducing computational overhead.
PockEngine significantly speeds up on-device training, performing up to 15 times faster on some hardware platforms, without compromising accuracy. [ more ]
MIT News | Massachusetts Institute of Technology
10 months ago
Artificial intelligence

Technique enables AI on edge devices to keep learning over time

Personalized deep-learning models can be trained on edge devices, eliminating the need to transmit sensitive user data to cloud servers.
PockEngine, a new on-device training method, enables fine-tuning of machine learning models directly on edge devices, improving privacy and reducing computational overhead.
PockEngine significantly speeds up on-device training, performing up to 15 times faster on some hardware platforms, without compromising accuracy. [ more ]
MIT News | Massachusetts Institute of Technology
10 months ago
Artificial intelligence

Technique enables AI on edge devices to keep learning over time

Personalized deep-learning models can be trained on edge devices, eliminating the need to transmit sensitive user data to cloud servers.
PockEngine, a new on-device training method, enables fine-tuning of machine learning models directly on edge devices, improving privacy and reducing computational overhead.
PockEngine significantly speeds up on-device training, performing up to 15 times faster on some hardware platforms, without compromising accuracy. [ more ]
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