Teknik Belgeler
Özellikler
Marka
CoralÜrün Tipi
Mikrodenetleyici Geliştirme Aracı
Kit Sınıflandırması
Mikrokontrolör Panosu
Kit Adı
Dev Board Micro
Teknoloji
ARM Cortex
Cihaz Çekirdeği
ARM Cortex-M7
İşlemci Aile Adı
ARM
İşlemci Parça Numarası
i.MX RT1176
İşlemci Tipi
Mikrodenetleyici
Standartlar/Onaylar
No
Menşe
China
Ürün Ayrıntıları
The Coral Dev Board Micro is a microcontroller board that benefits from a built-in camera,microphone, and Coral Edge TPU, allowing you to quickly prototype and deploy low-power embedded systems with on-device ML inferencing.
By combining the Cortex M4 and M7 processors with the Coral Edge TPU on this board, you can design systems that cascade from extreme low-power ML inferencing to more complex—yet still power-efficient—ML inferencing.
You can also expand the hardware with custom add-on boards using the high-density board-to-board connectors. The Edge TPU is a small ASIC designed by Google that accelerates tensor flow lite models in a power efficient manner. One Edge TPU is capable of performing 4 trillion operations per second. This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.
Stok bilgileri geçici olarak kullanılamıyor.
Teklif İsteyiniz
1
Teklif İsteyiniz
Stok bilgileri geçici olarak kullanılamıyor.
1
Teknik Belgeler
Özellikler
Marka
CoralÜrün Tipi
Mikrodenetleyici Geliştirme Aracı
Kit Sınıflandırması
Mikrokontrolör Panosu
Kit Adı
Dev Board Micro
Teknoloji
ARM Cortex
Cihaz Çekirdeği
ARM Cortex-M7
İşlemci Aile Adı
ARM
İşlemci Parça Numarası
i.MX RT1176
İşlemci Tipi
Mikrodenetleyici
Standartlar/Onaylar
No
Menşe
China
Ürün Ayrıntıları
The Coral Dev Board Micro is a microcontroller board that benefits from a built-in camera,microphone, and Coral Edge TPU, allowing you to quickly prototype and deploy low-power embedded systems with on-device ML inferencing.
By combining the Cortex M4 and M7 processors with the Coral Edge TPU on this board, you can design systems that cascade from extreme low-power ML inferencing to more complex—yet still power-efficient—ML inferencing.
You can also expand the hardware with custom add-on boards using the high-density board-to-board connectors. The Edge TPU is a small ASIC designed by Google that accelerates tensor flow lite models in a power efficient manner. One Edge TPU is capable of performing 4 trillion operations per second. This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.
