Devices: Wind River Linux, Arduino Robot, 96Boards and Jetson With GNU/Linux
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NVIDIA container runtime for Wind River Linux
Training and using AI models are tasks that demand significant computational power. Current trends are pointing more to deep neural networks, which include thousands, if not millions of operations per iteration. In the past year, more and more researchers have sounded the alarm on the exploding costs of deep learning. The computing power needed to do AI is now rising seven times faster than ever before [1]. These new needs are making hardware companies create hardware accelerators like Neural processing units, CPUs, and GPUs.
Embedded systems are not an exception to this transformation. We see every day intelligent traffic lights, autonomous vehicles, intelligent IoT devices, and more. The current direction is to have accelerators inside these embedded devices, Systems On-Chip mainly. Hardware developers have embedded small accelerators like GPUs, FPGAs, and more into SOCs, SOMs, and other systems. We call these modern systems: heterogeneous computing architectures.
The use of GPUs on Linux is not something new; we have been able to do so for many years. However, it would be great to accelerate the development and deployment of HPC applications. Containers enable portability, stability, and many other characteristics when deploying an application. For this reason, companies are investing so much in these technologies. For instance, NVIDIA recently started a project that enables CUDA on Docker [2].
One concern when dealing with containers is the loss of performance. However, when comparing the performance of the GPU with and without the containers environment, researchers found that no additional overhead is caused [3]. The consistency in the performance is one of the principal benefits of containers over virtual machines; accessing the GPU is done seamlessly as the kernel stays the constant.
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Spherical Quadruped Arduino Robot
[Greg06] started learning electronics the same way most of us did: buy a few kits, read a few tutorials, and try your hardest to put a few things together. Sound familiar? After a while, you noticed your skills started increasing, and your comfort level with different projects improved as well. Eventually, you try your hand at making your own custom projects and publishing your own tutorials.
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96Boards CE Extended SBC taps the quad -A7 Quectel SC20 4G module
Shiratech’s “SRT-96B-MAIN-SC20-E/A” is a 96Boards CE Extended SBC that runs Android on a Quectel SC20 module equipped with a quad -A7 Snapdragon 210, 4G LTE, GNSS, and WiFi/BT. The SBC features a CSI cam, a MEMs mic, accelerometer, and photometric sensor.
Last week we reported on Shiratech’s Linux-driven Stinger96 96Boards IoT Edition SBC with a dual Cortex-A8 STM32MP157 SoC and a Quectel BG96 LTE CAT-M1 radio for NB-IoT communications. We now see that Shiratech has posted specs for a SRT-96B-MAIN-SC20-E/A SBC, AKA the 96Board Main with SC-20. The board uses the 96Boards Consumer Edition Extended form factor and offers 4G LTE via a Quectel SC20 module that integrates a 1.1GHz, quad -A7 Snapdragon 210.
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Advantech Edge AI Computer and PoE NVR Feature NVIDIA Jetson Xavier NX SoM
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.
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