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Enabling Embedded Vision for all With Ultra-Low Power Image Classification

NOV
12
2020
12. NOV 2020

Lecture Embedded Forum > Low Power Design

17:20-17:40 h | Virtual

Convolutional neural networks continue to gain interest in IoT and mobile devices and are now gaining momentum at the edge. This session demonstrates the design and implementation of a complex neural networks on an ultra-low-power neural sensor processor with multicore ARM Cortex-M plus DSP, bringing manifold improvement in efficiency for low power embedded vision.

Subjects: Embedded systems

Speaker: Semir Haddad (Eta Compute)

Type: Lecture

Speech: English

Convolutional neural networks continue to gain interest in IoT and mobile devices and are now gaining momentum at the edge. This session demonstrates the design and implementation of a complex neural networks on an ultra-low-power neural sensor processor with multicore ARM Cortex-M plus DSP, bringing manifold improvement in efficiency for low power embedded vision.

Speaker,
Eta Compute

Semir Haddad

Semir Haddad

Eta Compute

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Informations

Speaker,
Eta Compute

Semir Haddad
Semir Haddad
Technical Marketing

Location

Eingang
Nord-West
ICM
Eingang
Nord
Eingang
West
Atrium
Eingang
Nord-Ost
Eingang
Ost
Conference
Center Nord
Freigelände
C1
C2
C3
C4
C5
C6
B0
B1
B2
B3
B4
B5
B6
A1
A2
A3
A4
A5
A6

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