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Computing devices that have been used for machine learning at the edge

posted Nov 15, 2020, 1:56 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Nov 15, 2020, 1:57 AM ]
Table 3 from M. G. S. Murshed, C. Murphy, D. Hou, N. Khan, G. Ananthanarayanan, and F. Hussain, “Machine Learning at the Network Edge: A Survey,” arXiv Prepr. arXiv1908.00080, pp. 1–28, 2019.

Device

GPU

CPU

RAM

Flash

memory

Power

consumption

Example

applications

Raspberry Pi

400MHz

VideoCore IV

Quad

Cortex A53 @ 1.2GHz

1 GB SDRAM

32 GB

2.5 Amp

video analysis [78, 114]

Coral Dev Board (Edge TPU)

GC7000 Lite

Graphics + Edge TPU coprocessor

Quad Cortex-A53, Cortex-M4F

1 GB LPDDR4

8 GB LPDDR4


5V DC

image processing [18]


SparkFun Edge


-

32-bit ARM

Cortex-M4F 48MHz

(with 96MHz

burst mode) processor


384KB


1MB


6uA/MHz


speech recognition [31]


Jetson TX1

Nvidia

Maxwell 256 CUDA

cores

Quad ARM A57/2 MB L2

4 GB

64 bit LPDDR4

25.6 GB/s

16 GB eMMC, SDIO, SATA


10-W

video, image analysis [68] [61]

robotics [33]


Jetson TX2

Nvidia Pascal

256 CUDA

cores

HMP Dual

Denver 2/2 MB L2 +

Quad ARM

A57/2 MB L2

8 GB

128 bit LPDDR4

59.7 GB/s


32 GB eMMC, SDIO, SATA


7.5-W


video, image analysis [68], [92]

robotics [24]

Intel Movidius Neural Compute Stick


High Performance VPU

Myriad 2 Vision Processing Unit


1 GB


4 GB

2 trillion

16-bit operations per second within

500 mW


classification [73] computer

vision [14, 43]

ARM ML

-

ARM ML

processor

1 GB

-

4 TOPs/W

(Tera Operations)

image, voice recognition [107]


RISC-V GAP8


-

nona-core

32-bit RISC-V

microprocessor

@250 MHz


16 MiB SDRAM


-


1 GOPs/mW


image, audio processing [35]

OpenMV

Cam

-

ARM 32-bit

Cortex-M7

512KB

2 MB

200mA

@ 3.3V

image

processing [6]

BeagleBone AI

-

Cortex-A15

Sitara AM5729 SoC with 4 EVEs

1 GB

16 GB

-

computer vision [25]

EMC3531

-

ARM Cortex-M3

NXP Coolflux DSP

-

-

-

audio, video analysis38

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