Nvidia Tegra X1 Category - Page 13 of 20 - CNX Software

May 22, 2020 by Jean-Luc Aufranc (CNXSoft) - No Comments

Advantech Edge AI Computer and PoE NVR Feature NVIDIA Jetson Xavier NX SoM

Advantech NVIDIA-Jetson-Xavier NX Edge AI Computer

Following the launch of NVIDIA Jetson Xavier NX SoM last year, we noted several third-party carrier boards and embedded PCs had been announced, and we expected more to come soon. Advantech has now unveiled two systems based on NVIDIA latest module with respectively MIC-710AIX edge AI computer and MIC-710IVX NVR system equipped with eight PoE ports. MIC-710AIX edge AI computer Specifications: SoM – NVIDIA Jetson Xavier NX with 6-core NVIDIA Carmel ARM v8.2 64-bit CPU, 6MB L2 + 4MB L3 caches 384-core NVIDIA Volta GPU with 48 Tensor Cores 2x NVDLA deep learning accelerators System Memory – 8GB 128-bit LPDDR4x Storage – 16 GB eMMC 5.1 flash Storage – M.2 2280 Key-M socket for SSD; SD card socket Video Output – HDMI up to 3840×2160 @ 60 Hz Networking – 2x Gigabit Ethernet (RJ45) via Intel i210IT controller USB – 1x USB 3.0 host port, 1x USB 2.0 host port, […]

May 16, 2020August 10, 2021 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

NVIDIA Jetson Developer Kits Comparison – Nano vs TX2 vs Xavier NX vs AGX Xavier

NVIDIA launched the Jetson Xavier NX developer kit yesterday, and I included a short comparison table in the announcement between Jetson Nano, TX2, Xavier NX, and AGX Xavier developer kits. But I thought it might be worthwhile to have a more detailed comparison in a separate post, so here we are. As expected, usually the more you spend on a board, the better the performance and features. The exception is Jetson TX2 which’s the same price as the new Jetson Xavier NX devkit but delivers about a fifth of the FP16 AI performance. So as today, there’s little reason to buy a TX2 board for a new project unless you need some of the required features that are missing on Xavier NX. Jean-Luc Aufranc (CNXSoft)Jean-Luc started CNX Software in 2010 as a part-time endeavor, before quitting his job as a software engineering manager, and starting to write daily news, and […]

May 15, 2020May 16, 2020 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

NVIDIA Introduces Jetson Xavier NX Developer Kit, and Cloud-Native Support

NVIDIA Jetson Xavier NX SoM was launched last month for $459. But while some third-party carrier boards were also announced at the time, the company had yet to offer Jetson Xavier NX Developer Kit as they did for Jetson Nano. But as GTC 2020 conference is now taking place in the kitchen of Jensen Huang, NVIDIA CEO, the company had plenty to announce including Jetson Xavier NX Developer Kit as well as “Cloud-Native” support for all Jetson boards and modules. NVIDIA Jetson Xavier NX Developer Kit Specifications: CPU – 6-core NVIDIA Carmel ARMv8.2 64-bit processor with 6 MB L2 + 4 MB L3 cache GPU – NVIDIA Volta architecture with 384 NVIDIA CUDA cores and 48 Tensor cores Accelerators 2x NVDLA Engines 7-Way VLIW Vision Processor Memory – 8 GB 128-bit LPDDR4x 51.2GB/s Storage – MicroSD slot, M.2 Key M socket for NVMe SSD Video Output – HDMI and DisplayPort […]

April 22, 2020July 3, 2020 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

AAEON Compact Fanless Embedded Box PC Features NVIDIA Jetson Xavier-NX or Nano SoM

NVIDIA Jetson Xavier-NX module was officially launched about a week ago, and at the time we noted several carrier boards and mini PCs were launched or announced from companies like D3 Engineering, Diamond Systems, and Connect Tech. We should expect many new Jetson Xavier-NX embedded mini PC announcements over the next few weeks and months, and AAEON has now announced two new similar compact fanless embedded box PCs namely Boxer-8221AI and BOXER-8251AI with the same designed but powered by respectively Jetson Nano and Jetson Xavier-NX modules. Both models should share the same specifications except for the module: SoM Boxer-8221AI – NVIDIA Jetson Nano with quad-core Arm Cortex-A57 processor @ 1.43 GHz, 128-core Maxwell GPU, 4GB LPDDR4, 16GB eMMC flash Boxer-8251AI – NVIDIA Jetson Xavier-NX with 6-core NVIDIA Carmel ARM v8.2 64-bit CPU, a 384-core NVIDIA Volta GPU with 48 Tensor Cores, 2x NVDLA deep learning accelerators (up to 21 TOPS […]

April 16, 2020April 16, 2020 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

NVIDIA Jetson Xavier NX SoM Launched for $459, Third-Party Carrier Boards & Edge Computers Available

NVIDIA announced the Jetson Xavier NX system-on-module last November with an NVIDIA Xavier SOC with 6 NVIDIA Carmel Arm v8.2 cores, a 384-core NVIDIA Volta GPU and two NVDLA deep learning accelerators for a combined 21 TOPS at 15 Watts. The 69.6 x 45 mm module also includes 8 GB LPDDR4x RAM and a 16GB eMMC flash with a 260-pin SO-DIMM providing various I/Os from PCIe to MIPI CSI and display interfaces such as HDMI and eDP. NVIDIA expected the module to be “available in March for $399 to companies looking to create high-volume production edge systems”, and at the time I thought it would be hard to purchase for simple mortals, but the company just sent an email announcing the launch of the module and it’s now listed for $459 on Arrow Electronics with no stock and a 16 weeks lead time. While there’s no Jetson Xavier NX development […]

January 17, 2020 by Jean-Luc Aufranc (CNXSoft) - 8 Comments

NVIDIA Jetson Nano Developer Kit-B01 Gets an Extra Camera Connector

Launched in March 2019, NVIDIA Jetson Nano developer kit offered an AI development platform for an affordable $99. The kit is comprised of Jetson Nano module and a carrier board, and the version I received last November ended with “A02”. Jetson Nano developer kit is now getting updated with B01 carrier board that adds an extra MIPI CSI connector and other few changes, including compatibility with NVIDIA Jetson Nano production module (with eMMC flash instead of MicroSD card). Jetson Nano developer kit-B01 specifications: B01 Jetson Nano CPU Module 128-core Maxwell GPU Quad-core Arm A57 processor @ 1.43 GHz System Memory – 4GB 64-bit LPDDR4 @ 25.6 GB/s Storage – microSD card slot Video Encode – 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265) Video Decode – 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 […]

December 9, 2019December 10, 2019 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

Testing NVIDIA Jetson Nano Developer Kit with and without Fan

A few weeks ago I received NVIDIA Jetson Nano for review together with 52Pi ICE Tower cooling fan which Seeed Studio included in the package, and yesterday I wrote a getting started guide showing how to setup the board, and play with inference samples leveraging the board’s AI capabilities. I’ll now test the board with the stock heatsink in both 5W and 10W modes, and see if thermal throttling does occur, and then I’ll fit the tower cooling fan to find out if we can extract more performance that way and how much lower the CPU temperature is. Jetson Nano Stress Tests with Stock Heatsink Let’s install SBC-Bench testing utility,

wget https://raw.githubusercontent.com/ThomasKaiser/sbc-bench/master/sbc-bench.sh
chmod +x sbc-bench.sh

1 2	wget https://raw.githubusercontent.com/ThomasKaiser/sbc-bench/master/sbc-bench.sh chmod +x sbc-bench.sh

check it’s properly installed,

sudo ./sbc-bench.sh -m
Time        CPU    load %cpu %sys %usr %nice %io %irq   Temp
15:05:06:  922MHz  0.05   5%   1%   2%   0%   0%   0%  35.0°C
15:05:11:  922MHz  0.13   3%   1%   1%   0%   0%   0%  35.0°C
15:05:16:  922MHz  0.12   3%   1%   1%   0%   0%   0%  34.8°C

sudo ./sbc-bench.sh -m

Time CPU load %cpu %sys %usr %nice %io %irq Temp

15:05:06: 922MHz 0.05 5% 1% 2% 0% 0% 0% 35.0°C

15:05:11: 922MHz 0.13 3% 1% 1% 0% 0% 0% 35.0°C

15:05:16: 922MHz 0.12 3% 1% 1% 0% 0% 0% 34.8°C

and run it in 5W mode:

sudo nvpmodel -m 1

sudo ./sbc-bench

sbc-bench v0.6.9

Memory performance:
memcpy: 3685.3 MB/s 
memset: 8555.4 MB/s 

7-zip total scores (3 consecutive runs): 2877,2885,2854

OpenSSL results:
type             16 bytes     64 bytes    256 bytes   1024 bytes   8192 bytes  16384 bytes
aes-128-cbc     284837.64k   525113.11k   639412.05k   706251.09k   728449.02k   729841.66k
aes-128-cbc     284316.13k   525028.93k   634287.70k   704675.84k   728088.58k   728973.31k
aes-192-cbc     262002.90k   458230.17k   544725.93k   588999.68k   604075.35k   604607.83k
aes-192-cbc     261583.66k   458583.96k   538986.92k   588138.84k   602303.15k   604067.16k
aes-256-cbc     247370.60k   405101.35k   466444.29k   501432.32k   512816.47k   513370.79k
aes-256-cbc     247650.51k   405270.40k   469783.72k   502266.54k   513187.84k   512977.58k

Full results uploaded to http://ix.io/23rg. Please check the log for anomalies (e.g. swapping
or throttling happenend) and otherwise share this URL.

sudo nvpmodel -m 1

sudo ./sbc-bench

sbc-bench v0.6.9

Memory performance:

memcpy: 3685.3 MB/s

memset: 8555.4 MB/s

7-zip total scores (3 consecutive runs): 2877,2885,2854

OpenSSL results:

type 16 bytes 64 bytes 256 bytes 1024 bytes 8192 bytes 16384 bytes

aes-128-cbc 284837.64k 525113.11k 639412.05k 706251.09k 728449.02k 729841.66k

aes-128-cbc 284316.13k 525028.93k 634287.70k 704675.84k 728088.58k 728973.31k

aes-192-cbc 262002.90k 458230.17k 544725.93k 588999.68k 604075.35k 604607.83k

aes-192-cbc 261583.66k 458583.96k 538986.92k 588138.84k 602303.15k 604067.16k

aes-256-cbc 247370.60k 405101.35k 466444.29k 501432.32k 512816.47k 513370.79k

aes-256-cbc 247650.51k 405270.40k 469783.72k 502266.54k 513187.84k 512977.58k

Full results uploaded to http://ix.io/23rg. Please check the log for anomalies (e.g. swapping

or throttling happenend) and otherwise share this URL.

The temperature never went over 44.5°C, and no throttling occurred. tegrastats during 7-zip multi-core test:

CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@43.5C PMIC@100C GPU@43.5C AO@52.5C thermal@43.5C POM_5V_IN 3348/2567 POM_5V_GPU 0/0 POM_5V_CPU 1549/912
RAM 1211/3956MB (lfb 515x4MB) SWAP 0/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@44C PMIC@100C GPU@43.5C AO@52C thermal@43.5C POM_5V_IN 3348/2568 POM_5V_GPU 0/0 POM_5V_CPU 1549/912
RAM 1239/3956MB (lfb 508x4MB) SWAP 0/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@44C PMIC@100C GPU@43.5C AO@52.5C thermal@43.5C POM_5V_IN 3348/2569 POM_5V_GPU 0/0 POM_5V_CPU 1549/913
RAM 1265/3956MB (lfb 502x4MB) SWAP 0/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@43.5C PMIC@100C GPU@43.5C AO@52.5C thermal@43.5C POM_5V_IN 3348/2571 POM_5V_GPU 0/0 POM_5V_CPU 1510/914

CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@43.5C PMIC@100C GPU@43.5C AO@52.5C thermal@43.5C POM_5V_IN 3348/2567 POM_5V_GPU 0/0 POM_5V_CPU 1549/912

RAM 1211/3956MB (lfb 515x4MB) SWAP 0/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@44C PMIC@100C GPU@43.5C AO@52C thermal@43.5C POM_5V_IN 3348/2568 POM_5V_GPU 0/0 POM_5V_CPU 1549/912

RAM 1239/3956MB (lfb 508x4MB) SWAP 0/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@44C PMIC@100C GPU@43.5C AO@52.5C thermal@43.5C POM_5V_IN 3348/2569 POM_5V_GPU 0/0 POM_5V_CPU 1549/913

RAM 1265/3956MB (lfb 502x4MB) SWAP 0/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [100%@1428,100%@1428,off,off] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 APE 25 PLL@41.5C CPU@43.5C PMIC@100C GPU@43.5C AO@52.5C thermal@43.5C POM_5V_IN 3348/2571 POM_5V_GPU 0/0 POM_5V_CPU 1510/914

Only two Cortex-A57 cores are used even under load, and power […]

December 7, 2019June 25, 2021 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

AI inference using Images, RTSP Video Stream on NVIDIA Jetson Nano Devkit

Last month I received NVIDIA Jetson Nano developer kit together with 52Pi ICE Tower Cooling Fan, and the main goal was to compare the performance of the board with the stock heatsink or 52Pi heatsink + fan combo. But the stock heatsink does a very good job of cooling the board, and typical CPU stress tests do not make the processor throttle at all. So I had to stress the GPU as well, as it takes some efforts to set it up all, so I’ll report my experience configuring the board, and running AI test programs including running objects detection on an RTSP video stream. Setting up NVIDIA Jetson Nano Board Preparing the board is very much like you’d do with other SBC’s such as the Raspberry Pi, and NVIDIA has a nicely put getting started guide, so I won’t go into too many details here. To summarize: Download the […]