Nvidia Jetson Nano Review

Introduction

NVIDIA announced the Jetson Nano Developer Kit at the 2019 NVIDIA GPU Technology Conference (GTC), a $99 [USD] computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability. NVIDIA® Jetson Nano Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts. Nvidia Jetson Nano is the Successor / Little Brother of the expensive Jetson TX1 at $499 [USD].

PlatformCPUGPUMemoryStorageMRP
Jetson TX1 (Tegra X1)4x ARM Cortex A57 @ 1.73 GHz256x Maxwell @ 998 MHz (1 TFLOP)4GB LPDDR4 (25.6 GB/s)16 GB eMMC$499
Jetson Nano4x ARM Cortex A57 @ 1.43 GHz128x Maxwell @ 921 MHz (472 GFLOPS)4GB LPDDR4 (25.6 GB/s)Micro SD$99

Jetson Nano has nearly Half the GPU Computation Power [ 472 GLOPS / 1 TFLOPS = 0.472 ] in Just 1/5th the Price of Jetson TX1. Per Dollar you get 4.7676 GFLOPS in Nvidia Nano vs 2.0040 GFLOPS in Jetson TX1. Sounds Awesome Right!! I will come to the Weaknesses also Keep Reading the Blog.

The Unboxing and Detailed Interface Videos

Jetson Nano vs Jetson TX1, TX2 and AGX Xavier

FeaturesJetson NanoJetson TX1Jetson TX2 / TX2iJetson AGX Xavier
CPUARM Cortex-A57 (quad-core) @ 1.43GHzARM Cortex-A57 (quad-core) @ 1.73GHzARM Cortex-A57 (quad-core) @ 2GHz +NVIDIA Denver2 (dual-core) @ 2GHzNVIDIA Carmel ARMv8.2 (octal-core) @ 2.26GHz(4x2MB L2 + 4MB L3)
GPU128-core NVIDIA Maxwell @ 921MHz256-core NVIDIA Maxwell @ 998MHz256-core NVIDIA Pascal @ 1300MHz512-core Volta @ 1377 MHz + 64 Tensor Cores
DL NVIDIA GPU support (CUDA, cuDNN, TensorRT) NVIDIA GPU support (CUDA, cuDNN, TensorRT) NVIDIA GPU support (CUDA, cuDNN, TensorRT) Dual NVIDIA Deep Learning Accelerators
Memory 4GB 64-bit LPDDR4 @ 1600MHz | 25.6 GB/s 4GB 64-bit LPDDR4 @ 1600MHz | 25.6 GB/s 8GB 128-bit LPDDR4 @ 1866Mhz | 58.3 GB/s 16GB 256-bit LPDDR4x @ 2133MHz | 137GB/s
Storage MicroSD card 16GB eMMC 5.1 32GB eMMC 5.1 32GB eMMC 5.1
Vision NVIDIA GPU support (CUDA, VisionWorks, OpenCV) NVIDIA GPU support (CUDA, VisionWorks, OpenCV) NVIDIA GPU support (CUDA, VisionWorks, OpenCV) 7-way VLIW Vision Accelerator
Encoder(1x) 4Kp30, (2x) 1080p60, (4x) 1080p30 (1x) 4Kp30, (2x) 1080p60, (4x) 1080p30 4Kp60, (3x) 4Kp30, (4x) 1080p60, (8x) 1080p30 (4x) 4Kp60, (8x) 4Kp30, (32x) 1080p30
Decoder4Kp60, (2x) 4Kp30, (4x) 1080p60, (8x) 1080p30 4Kp60, (2x) 4Kp30, (4x) 1080p60, (8x) 1080p30 (2x) 4Kp60, (4x) 4Kp30, (7x) 1080p60 (2x) 8Kp30, (6x) 4Kp60, (12x) 4Kp30
Camera 12 lanes MIPI CSI-2 | 1.5 Gbps per lane 12 lanes MIPI CSI-2 | 1.5 Gbps per lane 12 lanes MIPI CSI-2 | 2.5 Gbps per lane 16 lanes MIPI CSI-2 | 6.8125Gbps per lane
Display 2x HDMI 2.0 / DP 1.2 / eDP 1.2 | 2x MIPI DSI 2x HDMI 2.0 / DP 1.2 / eDP 1.2 | 2x MIPI DSI 2x HDMI 2.0 / DP 1.2 / eDP 1.2 | 2x MIPI DSI (3x) eDP 1.4 / DP 1.2 / HDMI 2.0 @ 4Kp60
WirelessM.2 Key-E site on carrier802.11a/b/g/n/ac 2×2 867Mbps | Bluetooth 4.0802.11a/b/g/n/ac 2×2 867Mbps | Bluetooth 4.1M.2 Key-E site on carrier
Ethernet 10/100/1000 BASE-T Ethernet 10/100/1000 BASE-T Ethernet 10/100/1000 BASE-T Ethernet 10/100/1000 BASE-T Ethernet
USB(4x) USB 3.0 + Micro-USB 2.0 USB 3.0 + USB 2.0 USB 3.0 + USB 2.0 (3x) USB 3.1 + (4x) USB 2.0
PCIePCIe Gen 2 x1/x2/x4PCIe Gen 2 x5 | 1×4 + 1×1PCIe Gen 2 x5 | 1×4 + 1×1 or 2×1 + 1×2PCIe Gen 4 x16 | 1×8 + 1×4 + 1×2 + 2×1
CAN NANADual CAN bus controller Dual CAN bus controller
Misc IO UART, SPI, I2C, I2S, GPIOs UART, SPI, I2C, I2S, GPIOs UART, SPI, I2C, I2S, GPIOs UART, SPI, I2C, I2S, GPIOs
Socket260-pin edge connector, 45x70mm 400-pin board-to-board connector, 50x87mm 400-pin board-to-board connector, 50x87mm 699-pin board-to-board connector, 100x87mm
Thermals -25°C to 80°C -25°C to 80°C -25°C to 80°C -25°C to 80°C
Power5/10W10W7.5W10/15/30W
Perf472 GFLOPS1 TFLOPS1.3 TFLOPS32 TeraOPS

Purchase and Unboxing

We bought it from the Nvidia India Website at a Price of Rs.8,899.00 and it arrived in few days. There was no Problem in Shipping or Ordering.

You don’t get much in the box for this Price point.

  • 80x100mm Reference Carrier Board, complete devkit with Module and Heatsink weighs 138 grams
  • Jetson Nano Module with a passive heatsink [ Upgradable to Active Cooling ]
  • Card Board Pop-Up Stand
  • Getting Started Paper Guide
  • No SD-Card, No Power Input Selection Jumper

The Jetson Nano Comes in a nicely designed cardboard box packaging it is very simplistic and aesthetically designed. The Dev Kit Comes in a Sealed Matt Finish Soft Black Static Safe Bag.

What You Will Need Extra

  • Power Supply
    • 5V⎓2A Micro-USB adapter ( Adafruit GEO151UB ) [ I Used One-Plus 6 Charger ]
    • 5V⎓4A DC Barrel Jack adapter, 5.5mm OD x 2.1mm ID x 9.5mm Length, Center-Positive ( Adafruit 1446 ) [ I Used a Belkin 5V⎓4A ]
  • MicroSD card ( 16GB UHS-1 Minumum ) [ OS will Occupy ~12.5GB ]
  • A Full HD 1080p or Above Monitor with HDMI or DP Input
  • A Computer to Flash the SD Card

Ports on the NVIDIA® Jetson Nano™ Developer Kit 

  1. microSD Card Slot for Main Storage
  2. 40-pin Expansion Header
  3. Micro-USB port for 5V⎓2A power input or for data
  4. Gigabit Ethernet Port
  5. USB 3.0 ports (x4)
  6. HDMI Output Port
  7. DisplayPort Connector
  8. DC Barrel Jack for 5V⎓4A Power Input
  9. MIPI CSI camera connector [ Supports Raspberry Pi CSI Camera ] ( Good Strategy by Nvidia to capture Market )

GPIO Nvidia Jetson Nano J41 Header

Nvidia has Intelligently kept the GPIO same as Raspberry Pi 3 B+ as this will help them to capture the market easily by supporting most of the Raspberry Pi Hats and Accessories Out of the Box. They also have placed the GPIO Header J41 is such a way that all the Hats protrude outwards as there is no Space to accommodate Hats on the PCB Side, unlike Raspberry Pi.

Sysfs GPIONamePinPinNameSysfs GPIO
 3.3 VDC
Power
125.0 VDC
Power
 
 I2C_2_SDA 
I2C Bus 1
345.0 VDC
Power
 
 I2C_2_SCL 
I2C Bus 1
56GND 
gpio216AUDIO_MCLK78UART_2_TX 
/dev/ttyTHS1
 
 GND910UART_2_RX 
/dev/ttyTHS1
 
gpio50UART_2_RTS1112I2S_4_SCLK
gpio79
gpio14SPI_2_SCK1314GND 
gpio194LCD_TE1516SPI_2_CS1
gpio232
 3.3 VDC
Power
1718SPI_2_CS0gpio15
gpio16SPI_1_MOSI1920GND 
gpio17SPI_1_MISO2122SPI_2_MISOgpio13
gpio18SPI_1_SCK2324SPI_1_CS0gpio19
 GND2526SPI_1_CS1gpio20
 I2C_1_SDA 
I2C Bus 0
2728I2C_1_SCL 
I2C Bus 0
 
gpio149CAM_AF_EN2930GND 
gpio200GPIO_PZ03132LCD_BL_PWMgpio168
gpio38GPIO_PE63334GND 
gpio76I2S_4_LRCK3536UART_2_CTSgpio51
gpio12SPI_2_MOSI3738I2S_4_SDINgpio77
 GND3940I2S_4_SDOUTgpio78

Nvidia Jetson Nano Benchmarks

Test Setup

Nvidia Nano Test Setup
  • Kingstone 32GB Micro-SD HC-I U1
  • Belkin 5V⎓4A AC Power Adapter
  • 150Mbps Ethernet Internet Connection
  • Active Air Cooling
  • Logitech Wireless USB Mouse and Keyboard Combo
  • HDMI Connection to 1080p Monitor

Phoronix Test Suite :

So I Started with Some of the Standard Test of Phoronix Test Suite. You can also run the same test by executing the below commands.

sudo apt-get install -y php-cli php-xml
Download PTS  https://www.phoronix-test-suite.com/ and  Install
run/ compare against article phoronix-test-suite benchmark 1809111-RA-ARMLINUX005
Accept for Dependency Installation and Wait a few hours
~/.phoronix-test-suite/test-results/
Comment Below Your Results
TestPi ZeroPi 3 BNanoTX1Notes
Tinymembench (memcpy)291129735013862
TTSIOD 3D Renderer15.6641.0045.05
7-Zip Compression205186335014526
C-Ray2357932851Seconds (lower is better)
Primesieve1543468401Seconds (lower is better)
AOBench333187165Seconds (lower is better)
FLAC Audio Encoding971.18387.09104.0178.86Seconds (lower is better)
LAME MP3 Encoding780352.66144.21113.14Seconds (lower is better)
Perl (Pod2html)5.38301.29450.71140.6007Seconds (lower is better)
PostgreSQL (Read Only)66401245016079
Redis (GET)34567213067568431484688
PyBench2434970806348ms (lower is better)
Scikit-Learn844489434Seconds (lower is better)

64-Bit Whetstone CPU Floating-Point Arithmetic

The Whetstone benchmark is a synthetic benchmark for evaluating the performance of computers. It was first written in Algol 60 in 1972 at TSU ( The Technical Support Unit of the Department of Trade and Industry – later part of the Central Computer and Telecommunications Agency or CCTA in the United Kingdom ).

I used the Code written by Roy Longbottom. He originally wrote the Code for 64-Bit Raspberry Pi ARM-v8 which is the same architecture as of Jetson Nano. I did a Local GCC build. You can download the code below:

There was no significant difference in MWIPS [ Million Whetstones Instructions Per Second ] Nvidia Nano is just 111MWIPS Faster than Raspberry Pi 3 B+.

cd to source directory 
gcc whets.c cpuidc.c -lm -lrt -O3 -o whetstonePi64 
Whetstone Results on Nvidia Nano
##############################################

Whetstone Single Precision C Benchmark  armv8 64 Bit, Sun May 12 14:31:50 2019

Loop content                   Result              MFLOPS      MOPS   Seconds

N1 floating point      -1.12475013732910156       329.588               0.070
N2 floating point      -1.12274742126464844       306.781               0.524
N3 if then else         1.00000000000000000                4246.247     0.029      1.00000000000000000
N4 fixed point         12.00000000000000000                1415.917     0.266     12.00000000000000000
N5 sin,cos etc.         0.49911010265350342                  28.007     3.556      0.49911010265350342
N6 floating point       0.99999982118606567       230.691               2.799
N7 assignments          3.00000000000000000                 944.467     0.234      3.00000000000000000
N8 exp,sqrt etc.        0.75110864639282227                  17.620     2.527      0.75110864639282227

MWIPS                                            1196.315              10.006

From File /proc/version
Linux version 4.9.140-tegra (buildbrain@mobile-u64-3531) 
(gcc version 7.3.1 20180425 [linaro-7.3-2018.05 revision d29120a424ecfbc167ef90065c0eeb7f91977701]
(Linaro GCC 7.3-2018.05) ) #1 SMP PREEMPT Wed Mar 13 00:32:22 PDT 2019
Whetstone Results on Raspberry Pi 3 B+
##############################################

Whetstone Single Precision C Benchmark  armv8 64 Bit, Sat May 25 19:33:45 2019


Loop content                   Result              MFLOPS      MOPS   Seconds

N1 floating point      -1.12475013732910156       390.682               0.053
N2 floating point      -1.12274742126464844       421.525               0.346
N3 if then else         1.00000000000000000               156858576.000     0.000
N4 fixed point         12.00000000000000000                1738.984     0.196
N5 sin,cos etc.         0.49911010265350342                  21.011     4.292
N6 floating point       0.99999982118606567       348.137               1.680
N7 assignments          3.00000000000000000                1391.264     0.144
N8 exp,sqrt etc.        0.75110864639282227                  12.314     3.275

MWIPS                                            1085.542               9.986

From File /proc/version
Linux version 4.14.79-v7+ (dc4@dc4-XPS13-9333) (gcc version 4.9.3 
(crosstool-NG crosstool-ng-1.22.0-88-g8460611)) 
#1159 SMP Sun Nov 4 17:50:20 GMT 2018

LINPACK Double Precision

The LINPACK Benchmarks are a measure of a system’s floating point computing power. Introduced by Jack Dongarra, they measure how fast a computer solves a dense n by n system of linear equations Ax = b, which is a common task in engineering. The performance measured by the LINPACK benchmark consists of the number of 64-bit floating-point operations, generally additions and multiplications, a computer can perform per second, also known as FLOPS. However, a computer’s performance when running actual applications is likely to be far behind the maximal performance it achieves running the appropriate LINPACK benchmark.

Compared to Raspberry Pi 3 B+ we have 4.2 Times LINPACK FLOPS Performance.

LINPACK Results on Nvidia Nano
########################################################

 Linpack Double Precision Unrolled Benchmark n @ 100
 Optimisation armv8 64 Bit, Sun May 12 14:37:08 2019

 Speed     912.26 MFLOPS

########################################################

From File /proc/version
Linux version 4.9.140-tegra (buildbrain@mobile-u64-3531) 
(gcc version 7.3.1 20180425 [linaro-7.3-2018.05 revision d29120a424ecfbc167ef90065c0eeb7f91977701] 
(Linaro GCC 7.3-2018.05) ) #1 SMP PREEMPT Wed Mar 13 00:32:22 PDT 2019
LINPACK Results on Raspberry Pi 3 B+
########################################################

 Linpack Double Precision Unrolled Benchmark n @ 100
 Optimisation armv8 64 Bit, Sat May 25 19:43:16 2019

 Speed     216.60 MFLOPS

########################################################

From File /proc/version
Linux version 4.14.79-v7+ (dc4@dc4-XPS13-9333) 
(gcc version 4.9.3 (crosstool-NG crosstool-ng-1.22.0-88-g8460611)) 
#1159 SMP Sun Nov 4 17:50:20 GMT 2018

Bus Speed Benchmark

This benchmark is designed to identify reading data in bursts over buses. The program starts by reading a word (4 bytes) with an address increment of 32 words (128 bytes) before reading another word. The increment is reduced by half on successive tests, until all data is read.

Maximum MB/second data transfer speed is calculated as bus clock MHz x 2 for Double Data Rate (DDR) x bus width (at this time 4 bytes ARM, 8 bytes Intel) x number of memory channels. However, some of these specifications can be misleading and maximum speed options might not be provided on a particular platform. Where the maximum is not provided, there can be confusion as to whether specified MHz is raw bus clock speed or included DDR consideration.

Compared to Raspberry Pi 3 B+ we have nearly double the Bus Speed.

Bus Speed Results on Nvidia Jetson Nano
#####################################################

   BusSpeed armv8 64 Bit Sun May 12 14:39:49 2019
 
    Reading Speed 4 Byte Words in MBytes/Second
  Memory  Inc32  Inc16   Inc8   Inc4   Inc2   Read
  KBytes  Words  Words  Words  Words  Words    All

      16    831   1118   2043   3333   4882   7105
      32   1531   1848   2827   4088   5144   7533
      64    584    778   1598   2890   4590   6672
     128    624    837   1594   2837   4454   6612
     256    609    839   1623   2875   4555   6618
     512    613    846   1630   2801   4314   6619
    1024    615    842   1612   2701   4496   6526
    4096    188    195    594   1111   2007   3857
   16384    163    172    549   1117   2025   3931
   65536    164    172    557   1107   2006   4019

        End of test Sun May 12 14:39:59 2019

From File /proc/version
Linux version 4.9.140-tegra (buildbrain@mobile-u64-3531) (gcc version 7.3.1 20180425
[linaro-7.3-2018.05 revision d29120a424ecfbc167ef90065c0eeb7f91977701] (Linaro GCC 7.3-2018.05) )
#1 SMP PREEMPT Wed Mar 13 00:32:22 PDT 2019
Bus Speed Results on Raspberry Pi 3 B+
#####################################################

   BusSpeed armv8 64 Bit Sat May 25 19:43:56 2019
 
    Reading Speed 4 Byte Words in MBytes/Second
  Memory  Inc32  Inc16   Inc8   Inc4   Inc2   Read
  KBytes  Words  Words  Words  Words  Words    All

      16   1147   1181   2896   4674   4852   4959
      32    718    816   1440   2468   3555   4253
      64    679    719   1340   2303   3407   4091
     128    639    650   1194   2117   3267   4053
     256    577    622   1113   2028   2630   3977
     512    368    400    762   1421   2477   3529
    1024    116    153    300    588   1132   2271
    4096    105    140    275    531   1066   1915
   16384    130    138    274    529   1083   1976
   65536    130    139    272    528   1074   2015

        End of test Sat May 25 19:44:07 2019

From File /proc/version
Linux version 4.14.79-v7+ (dc4@dc4-XPS13-9333) 
(gcc version 4.9.3 (crosstool-NG crosstool-ng-1.22.0-88-g8460611))
#1159 SMP Sun Nov 4 17:50:20 GMT 2018

Blender Rendering Benchmark on Nvidia Jetson Nano

When I saw that it has a 64-bit Quad-core ARM A57 @ 1.43GHz CPU and a 128-core NVIDIA Maxwell @ 921MHz GPU one of the Use Case that came to my mind is, can this system be a cheap alternative for Rendering? I compared the Rendering Performance of Nvidia Nano with Processor Intel(R) Core(TM) i3-3110M CPU @ 2.40GHz, 2400 Mhz, 2 Core(s), 4 Logical Processor(s) and GPU AMD Radeon HD 7600M Series [ 600Mhz ].

For this Benchmark, I have used Blender v2.79b in Both the Systems. I have used the Sample File without changing any parameters Blender 2.74 – Fishy Cat. It took nearly Double Time in Jetson Nano to render the same Frame.

Nvidia Jeston Nano Blender Render
Nvidia Jeston Nano Blender Render
Jetson Nano Blender CPU Render

Time Taken : 05:13:12 Memory Used : 464.32MB

Jetson Nano Blender GPU Render [ CUDA Enabled ]

Time Taken : 05:26:48 Memory Used : 464.32MB

Intel(R) Core(TM) i3-3110M CPU Blender CPU Render

Time Taken : 02:47:34 Memory Used : 466.65MB

AMD Radeon HD 7600M GPU Render

Time Taken : 02:43:73 Memory Used : 465.22MB

Deep Learning Inference Benchmarks [ Credit: Nvidia Blog ]

Jetson Nano can run a wide variety of advanced networks, including the full native versions of popular ML frameworks like TensorFlow, PyTorch, Caffe/Caffe2, Keras, MXNet, and others. These networks can be used to build autonomous machines and complex AI systems by implementing robust capabilities such as image recognition, object detection and localization, pose estimation, semantic segmentation, video enhancement, and intelligent analytics.

The figure shows results from inference benchmarks across popular models available online. See here for the instructions to run these benchmarks on your Jetson Nano. The inferencing used batch size 1 and FP16 precision, employing NVIDIA’s TensorRT accelerator library included with JetPack 4.2. Jetson Nano attains real-time performance in many scenarios and is capable of processing multiple high-definition video streams.

Jetson Nano deep learning inference performance chart
Network Model Credit :: Nvidia Blog
ModelApplicationFrameworkNVIDIA Jetson NanoRaspberry Pi 3Raspberry Pi 3 + Intel Neural Compute Stick 2Google Edge TPU Dev Board
ResNet-50
(224×224)
ClassificationTensorFlow36 FPS1.4 FPS16 FPSDNR
MobileNet-v2
(300×300)
ClassificationTensorFlow64 FPS2.5 FPS30 FPS130 FPS
SSD ResNet-18 (960×544)Object DetectionTensorFlow5 FPSDNRDNRDNR
SSD ResNet-18 (480×272)Object DetectionTensorFlow16 FPSDNRDNRDNR
SSD ResNet-18 (300×300)Object DetectionTensorFlow18 FPSDNRDNRDNR
SSD Mobilenet-V2 (960×544)Object
Detection
TensorFlow8 FPSDNR1.8 FPSDNR
SSD Mobilenet-V2 (480×272)Object DetectionTensorFlow27 FPSDNR7 FPSDNR
SSD Mobilenet-V2(300×300)Object DetectionTensorFlow39 FPS1 FPS11 FPS48 FPS
Inception V4(299×299)ClassificationPyTorch11 FPSDNRDNR9 FPS
Tiny YOLO V3(416×416)Object DetectionDarknet25 FPS0.5 FPSDNRDNR
OpenPose(256×256)Pose EstimationCaffe14 FPSDNR5 FPSDNR
VGG-19 (224×224)ClassificationMXNet10 FPS0.5 FPS5 FPSDNR
Super Resolution (481×321)Image ProcessingPyTorch15 FPSDNR0.6 FPSDNR
Unet(1x512x512)SegmentationCaffe18 FPSDNR5 FPSDNR

Nvidia Jetson Nano Review and FAQ

Nvidia Jetson Nano is an awesome device with a lot of processing power. No device is perfect and it has some Pros and Cons Involved in it.

  • PROS
    • Cheap Just 99$ or Rs8,899. More Processing Power and HW Resource Per Dollar compared to Raspberry Pi.
    • 4 x USB 3.0 A. For Better Connectivity to Depth Cameras and External Accessories.
    • 4K Video Processing Capability Unlike Raspberry Pi
    • Multiple Monitor can be Hooked Up
    • Selectable Power Source
  • CONS
    • Limited RAM Bandwidth of 25.6 GB/s Still Better than Raspberry Pi
    • microSD as Main Storage Device Limits Disk Performance. Using the M.2 Key-E with PCIe x1 Slot for SSD or USB HDD / SSD can Solve this Problem, Check this Solution.
    • Less Support for Softwares as Architecture is AArch64, many software will not work out of the box.
Is Nvidia Jetson Nano better than Raspberry Pi?

Definitely Yes, It Has a Better CPU, GPU and RAM. It is much fluid to use and the difference in performance is evident if you use then side by side. Due to the Extra RAM Web Browsing and other activities are more responsive than Raspberry Pi 3 B+.

Can Nvidia Jetson Nano be used as a Linux PC?

Yes, It can be Used. The 4GB RAM makes it very usable. I highly suggest transferring the ROOT File System to an M.2 SSD. There will be less software available for AArch64 Architecture.

What is the Cost of Nvidia Jeston in India?

Nvidia Jetson nano Cost Rs8,899 Only.

Can I do GPU Rendering on Nvidia Jetson?

Theoritically you can use this but Intel Processor or Other Nvidia PC GPU are Far Better.

Can I Connect a External GPU to the PCI Slot Provided?

As of Now not possible as there is no Driver Support form Nvidia. You can wait for their later releases. You can later connect a Volta GPU to this and can train your Neural Network.

What Devices I can Connect to the M.2 Slot in Nvidia Jetson Nano?

Intel Wireless Card, SSD, PCI Based Expansion Devices anything that is PCI Based [ If Driver Exist ]. You will Need a PCI Extender as there is No Space Available for Bigger Devices other than Intel Wireless Card.


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Categories: Nvidia

Crazy Engineer

MAKER - ENGINEER - YOUTUBER

2 Comments

Pawan · August 10, 2019 at 11:57 am

Hi Arnab

I have a question regarding the fps of a model , you mention that jetson nano has 472 GFLOPS but yet the SSD Mobilenet-V2(300×300) runs only at 39 FPS even though the model only uses 1 GFLOPS (source https://github.com/albanie/convnet-burden/blob/master/reports/ssd-pascal-mobilenet-ft.md), shouldn’t the fps be higher.
Am i missing something.

    Crazy Engineer · August 11, 2019 at 2:29 pm

    input size feature size feature memory flops
    150 x 150 1 x 1 x 128 1 GB 39 GFLOPs
    300 x 300 1 x 1 x 128 4 GB 146 GFLOPs
    450 x 450 1 x 1 x 128 10 GB 336 GFLOPs
    600 x 600 2 x 2 x 128 17 GB 574 GFLOPs
    750 x 750 2 x 2 x 128 27 GB 890 GFLOPs
    900 x 900 2 x 2 x 128 39 GB 1 TFLOPs

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