Nvidia Jetson background

NVidia Jetson Family Solutions

Following the global technology trends, we are aiming for bringing AI to the edge. To do this, we collaborate with the best – NVidia has already created a golden standard for on-device AI with its Jetson product family.

Using high-performance & energy-efficient NVidia Jetson Nano, Jetson TX2, Jetson Xavier NX, or Jetson AGX Xavier AI computing platforms in FPGA programming projects, we provide premier hardware design services.

About NVidia Jetson

NVidia Jetson products are great embedded solutions that are scalable and power-efficient. Each system represents a comprehensive SoM (System-on-Module), containing a CPU, GPU, DRAM, flash storage, and PMIC.

The whole product family has a common software stack, which adds to easy and widespread deployment. NVidia provides Jetson platforms with the same Jetpack SDK, including BSP (board support package), Linux OS, and NVidia CUDA. It is compatible with 3rd-party platforms.

NVidia Jetson module

Our services

We offer a range of development services using NVIDIA Jetson platforms: from custom software/hardware design to turnkey product development, whether for startups, niche companies, market leaders or enterprises.

Electronic hardware design on Jetson
Power & performance optimization
Mobile & Cloud apps development
Audio & Video
Firmware & BSP development
ML/DL algorithms deployment

Example of our Nvidia Jetson-based project

3G-SDI stream H 265 compression

3G-SDI stream H.265 compression

Tags: Kintex7, Linux, PCI-e Jetson Nano, Drivers, H.265, SDI 
The device compresses a 3G-SDI input stream with the H.265 encoder. A V4L2 driver adapts the PCIe data stream to be processed by GStreamer and NVidia HW codec. Linux controls the output bitrate by network throughput estimation (QoS). The PCIe links and delivers a low latency encoding chain.

Edge AI solutions application areas

Smart Surveillance
Industrial robotics
Audio analytics
Predictive maintenance
NVidia Jetson board
Body monitoring
Autonomous vehicles
Face recognition
Expression analysis

NVidia Jetson module specification comparison

Jetson NanoJetson TX2 SeriesJetson Xavier NXJetson AGX Xavier
TX2 NXTX2 4GBTX2TX2i
472 GFLOPS1.33 TFLOPS1.26 TFLOPS21 TOPS32 TOPS
128-core NVIDIA Maxwell GPU256-core NVIDIA Pascal GPU384-core NVIDIA Volta GPU with 48 Tensor Cores512-core NVIDIA Volta GPU with 64 Tensor Cores
Quad-core ARM®Cortex®-A57MPCore processorDual-core Denver 2 64-bit CPU and quad-core Arm® Cortex®-A57 MPCore processor6-core NVIDIA Carmel ARM®v8.2 64-bit CPU 6MB L2 + 4MB L38-core NVIDIA Carmel Arm®v8.2 64-bit CPU 8MB L2 + 4MB L3
4 GB 64-bitLPDDR4 25.6GB/s4 GB 128-bit LPDDR4 51.2GB/s8 GB 128-bitLPDDR4 59.7GB/s8 GB 128-bitLPDDR4 (ECC Support) 51.2GB/s8 GB 128-bit LPDDR4x 51.2GB/s32 GB 256-bitLPDDR4x 136.5GB/s
16 GB eMMC 5.116 GB eMMC 5.132 GB eMMC 5.132 GB eMMC 5.116 GB eMMC 5.132GB eMMC 5.1
5W | 10W7.5W | 15W10W | 20W10W | 15W10W | 15W | 30W
1 x4
(PCIe Gen2)
1 x1 + 1 x2
(PCIe Gen2)
1 x1 + 1 x4 OR 1 x1 + 1 x1 + 1 x2
(PCIe Gen2)
1 x1 (PCIe Gen3) + 1 x4 (PCIe Gen4)*1 x8 + 1 x4 + 1 x2 + 2 x1
(PCIe Gen4, Root Port & Endpoint)
Up to 4 cameras
12 lanes MIPI CSI-2
D-PHY 1.1 (up to 18 Gbps)
Up to 5 cameras
(12 via virtual channels)
12 lanes MIPI CSI-2
D-PHY 1.2 (up to 30 Gbps)
Up to 6 cameras
(12 via virtual channels)
12 lanes MIPI CSI-2
D-PHY 1.2 (up to 30 Gbps)
Up to 6 cameras
(24 via virtual channels)
14 lanes MIPI CSI-2
D-PHY 1.2 (up to 30 Gbps)
Up to 6 cameras
(36 via virtual channels)
16 lanes MIPI CSI-2 | 8 lanes SLVS-EC
D-PHY 1.2 (up to 40 Gbps)
C-PHY 1.1 (up to 91 Gbps)
1x 4K30 (H.265)
2x 1080p60 (H.265)
1x 4K60 (H.265)
3x 4K30 (H.265)
4x 1080p60 (H.265)
2x 4K30 (H.265)
6x 1080p60 (H.265)
4x 4K60 (H.265)
16x 1080p60 (H.265)
32x 1080p30 (H.265)
1x 4K60 (H.265)
4x 1080p60 (H.265)
2x 4K60 (H.265)
7x 1080p60 (H.265)
14x 1080p30 (H.265)
2x 4K60 (H.265)
12x 1080p60 (H.265)
16x 1080p30 (H.265)
2x 8K30 (H.265)
6x 4K60 (H.265)
26x 1080p60 (H.265)
72x 1080p30 (H.265)
2 multi-mode DP 1.2/eDP 1.4/HDMI 2.0
1 x2 DSI (1.5Gbps/lane)
2 multi-mode DP 1.2/eDP 1.4/HDMI 2.0
1x 2 DSI (1.5Gbps/lane)
2 multi-mode DP 1.2/eDP 1.4/HDMI 2.0
2 x4 DSI (1.5Gbps/lane)
2 multi-mode DP 1.4/eDP 1.4/HDMI 2.0
No DSI support
3 multi-mode DP 1.4/eDP 1.4/HDMI 2.0
No DSI support
--2x NVDLA Engines2x NVDLA Engines
--7-Way VLIW Vision Processor7-Way VLIW Vision Processor
10/100/1000 BASE-T Ethernet10/100/1000 BASE-T Ethernet10/100/1000 BASE-T Ethernet, WLAN10/100/1000 BASE-T Ethernet10/100/1000 BASE-T Ethernet10/100/1000 BASE-T Ethernet
69.6 mm x 45 mm
260-pin SO-DIMM connector
69.6 mm x 45 mm
260-pin SO-DIMM connector
87 mm x 50 mm
400-pin connector
69.6 mm x 45 mm
260-pin SO-DIMM connector
100 mm x87 mm
699-pin connector

 

Our tech map in FPGA

Specialized tools

Vitis/Vivado, Quartus Prime, Diamond, Libero, Matlab

Software platforms

NVidia Jetson, Alveo, OpenVINO, TensorFlow, Keras, Caffe

Tools & Languages

Verilog, VHDL, VivadoHLS, Simulink/HDL Coder, С/C++, Python

Hardware design

High-speed PCBs, DDR4, JESD204b, HDMI, SDI, SI, PI, Thermo modeling

Platforms

Zynq US+, RFSoC, Cyclone10, ECP5, MPF500

Transceivers

AD9361, AD9371, ADRV9009, Radars, Custom AFE, Antenas

Network protocols

DPDK, UDP 10G, TCP 10G, TAPs, L1/L2 IP cores

Networking

1G, 10G, 25G/40G, 100G

Do you need a quote for your Edge AI project?
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