NVIDIA Jetson Family Solutions
Following the global technology trends, we aim to bring 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.
As an FPGA design company, we use high-performance and energy-efficient NVIDIA Jetson Nano, TX2, Xavier NX, and AGX Xavier AI computing platforms to provide top-notch hardware design services.
About NVIDIA Jetson
NVIDIA Jetson products are scalable and power-efficient embedded solutions for SoC FPGA and AI-based FPGA development. Each system represents a comprehensive SoM (System-on-Module) containing a CPU, GPU, DRAM, flash storage, and PMIC.
The whole product family has a standard 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.
Our Services
We offer end-to-end development services – from custom software and hardware design to turnkey product development. Our team creates robust NVIDIA Jetson-based FPGA solutions tailored for startups, niche companies, and market leaders.
Example of Our NVIDIA Jetson-Based Project
3G-SDI stream H.265 compression
Tags: Kintex7, Linux, PCI-e Jetson Nano, Drivers, H.265, SDIÂ
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 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
NVIDIA Jetson Module Specification Comparison
| Nano | TX2 Series | Xavier NX | AGX Xavier | |||
| TX2 NX | TX2 4GB | TX2 | TX2i | |||
| 472 GFLOPS | 1.33 TFLOPS | 1.26 TFLOPS | 21 TOPS | 32 TOPS | ||
| 128-core Maxwell™GPU | 256-core Pascal™ GPU | 384-core Volta™GPU with 48 Tensor Cores | 512-core Volta™GPU with 64 Tensor Cores | |||
| Quad-core ARM®Cortex®-A57MPCore processor | Dual-core Denver 2 64-bit CPU and quad-core Arm® Cortex®-A57 MPCore processor | 6-core Carmel ARM®v8.2 64-bit CPU 6MB L2 + 4MB L3 | 8-core Carmel ARM®v8.2 64-bit CPU 8MB L2 + 4MB L3 | |||
| 4 GB 64-bitLPDDR4 25.6GB/s | 4 GB 128-bit LPDDR4 51.2GB/s | 8 GB 128-bitLPDDR4 59.7GB/s | 8 GB 128-bitLPDDR4 (ECC Support) 51.2GB/s | 8 GB 128-bit LPDDR4x 51.2GB/s | 32 GB 256-bitLPDDR4x 136.5GB/s | |
| 16 GB eMMC 5.1 | 16 GB eMMC 5.1 | 32 GB eMMC 5.1 | 32 GB eMMC 5.1 | 16 GB eMMC 5.1 | 32GB eMMC 5.1 | |
| 5W | 10W | 7.5W | 15W | 10W | 20W | 10W | 15W | 10W | 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 Engines | 2x NVDLA Engines | |||
| - | - | 7-Way VLIW Vision Processor | 7-Way VLIW Vision Processor | |||
| 10/100/1000 BASE-T Ethernet | 10/100/1000 BASE-T Ethernet | 10/100/1000 BASE-T Ethernet, WLAN | 10/100/1000 BASE-T Ethernet | 10/100/1000 BASE-T Ethernet | 10/100/1000 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 | ||
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Technologies We Use in NVIDIA-based FPGA Projects
Vitis/Vivado, Quartus Prime, Diamond, Libero, Matlab
NVidia Jetson, Alveo, OpenVINO, TensorFlow, Keras, Caffe
Verilog, VHDL, VivadoHLS, Simulink/HDL Coder, С/C++, Python
High-speed PCBs, DDR4, JESD204b, HDMI, SDI, SI, PI, Thermo modeling
Zynq US+, RFSoC, Cyclone10, ECP5, MPF500
AD9361, AD9371, ADRV9009, Radars, Custom AFE, Antenas
DPDK, UDP 10G, TCP 10G, TAPs, L1/L2 IP cores
1G, 10G, 25G/40G, 100G
Our FPGA Design Projects
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