Nvidia Tegra X1 Category - Page 10 of 16 - CNX Software

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 […]

November 27, 2019 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

AAEON BOXER-8220AI Embedded Box PC Features NVIDIA Jetson Nano, 5 Gigabit Ethernet Ports

AAEON has launched several AI Boxer-8000 series embedded box PCs based on Intel processors plus AI accelerator, or NVIDIA Jetson TX2. The company has now introduced a new model – BOXER-8220AI – based on NVIDIA Jetson Nano module, and equipped with five Gigabit Ethernet ports. AAEON BOXER-8220AI specifications: SoM (CPU/Memory/Storage) – NVIDIA Jetson Nano with quad-core Arm Cortex-A57 MPCore processor @ 1.43 GHz, 128-core Maxwell GPU. 4GB LPDDR4, 16GB eMMC flash or MicroSD card Video Output – HDMI 2.0 Connectivity – 5x Gigabit Ethernet ports USB – 4x USB 3.0 ports, 1x Micro USB to flash the OS Serial – 2x RS-232 Misc – Power button, recovery button, power LED Power Supply – 10-24V DC via 2-pin terminal block Dimensions – 154 x 101 x 30 mm Weight – 1 kg Temperature Range – Operating: -20°C ~ 60°C, according to IEC60068-2 with 0.5 m/s AirFlow; storage: -45°C ~ 80°C Storage […]

November 11, 2019May 16, 2020 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

NVIDIA Jetson Nano Review with 52Pi ICE Tower Cooling Fan – Part 1: Unboxing

Jetson Nano Review with ICE Tower Cooling Fan

If you remember soon after Raspberry Pi 4 launch, there were talks about the SBC overheating under load, and depending on room temperature and workload a heatsink may be needed for the board to perform optimally at all times. This gave birth to “interesting” solutions such as 52Pi ICE Tower Cooling Fan, an oversized cooling solution for Raspberry Pi 4. It does the job however, and it allows me to overclock Raspberry Pi 4 to 2.0 GHz while keeping the CPU temperature under 55°C in a room at 28°C. But the latest Raspberry Pi Foundation board is not the only SBC to suffer from overheating, as at least one user noticed the board would just shutdown under load. The solution was to switch from 10W mode to 5W mode, not an ideal solution since it’s also lowering performance. But 52Pi is coming to the rescue again, as they adapted their […]

November 7, 2019November 7, 2019 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

NVIDIA Jetson Xavier NX SoM Delivers up to 21 TOPS for AI Workloads at the Edge

NVIDIA has just announced Jetson Xavier NX system-on-module, with the company claiming it is the “world’s smallest, most powerful AI supercomputer for robotic and embedded computing devices at the edge” with a 70x45mm “Jetson Nano” form factor, and delivering either up to 14 TOPS at 10 Watts or 21 TOPS at 15 Watts. The company expects the module to be used in small commercial robots, drones, intelligent high-resolution sensors for factory logistics and production lines, optical inspection, network video recorders, portable medical devices, and other industrial IoT systems. Jetson Xavier NX specifications: SoC – NVIDIA Xavier with 6-core NVIDIA Carmel ARM v8.2 64-bit CPU, 6MB L2 + 4MB L3 caches, and a 384-core NVIDIA Volta GPU with 48 Tensor Cores, 2x NVDLA deep learning accelerators delivering up to 21 TOPS at 15 Watts System Memory – 8 GB 128-bit LPDDR4x @ 51.2GB/s Storage – 16 GB eMMC 5.1 flash Video […]

November 5, 2019 by Stephen Vicinanza - No Comments

AuverCare is a Predictive AI 24/7 Monitor for Patients and Elderly

Information on the Internet for Monitoring Patients and Elderly If you are in the healthcare field or caring for an elderly or disabled person, you know all about trying to keep them safe. The internet is crowded with camera and audio monitors, wearable fall alert alarms, and the emergency services connected panic button. All these devices have one problem, they only work after there is a problem. What is Predictive AI? There has been a great deal of chatter since the 2016 announcement of a future predicting AI platform. The article reported on this platform was Predicting the Future – especially use cases such as falls by the elderly. With the advances in the field of AI and object recognition and behavior that is based on data gathered from a variety of sensors, it is not really a stretch to see a predictive healthcare monitor come onto the scene. Predictive […]