Introduction
We offer an AI-DL Lab designed to explore and develop skills in Machine Learning, Deep Learning, Deployment/Inference on Embedded Hardware, and Robotics. The lab provides configurable options tailored for both research activities and student training.
The AI–DL Laboratory is equipped with advanced hardware and software setups to facilitate skill development in Machine Learning, Deep Learning, Deployment Techniques on Embedded GPU Boards, and Robotics.
A structured set of experiments designed to cover both fundamental and advanced AI concepts. Access to Embedded GPU boards for hands-on practice in deployment techniques.
AI/ML Machine Setup: AI Server
High-performance GPU cards with complete tools, libraries, frameworks, and datasets for Machine Learning, Deep Learning, and Robotics.
Technical Specifications:
Processor: 2x Intel Xeon Silver 4516Y (24C/48T, 2.2-3.7GHz, 185W)
RAM: 512GB DDR5 ECC
Storage: 960GB NVMe SSD + 2x 8TB HDD
Network: 2x 10G Ethernet
Expansion: PCIe x8 (front), PCIe x16 (rear)
Form Factor: 4U Rack Server supporting up to 8 GPU cards
GPU Options
| Option | GPU Model | Memory per GPU | Configuration | Total GPU Memory |
|---|---|---|---|---|
| 1 | NVIDIA RTX Pro 4000 Blackwell | 24 GB | 4x / 8x | 96 GB / 192 GB |
| 2 | NVIDIA RTX Pro 4500 Blackwell | 32 GB | 4x / 8x | 128 GB / 256 GB |
| 3 | NVIDIA RTX 5000 ADA | 32 GB | 4x / 8x | 128 GB / 256 GB |
| 4 | NVIDIA RTX Pro 5000 Blackwell | 48 GB | 4x / 8x | 192 GB / 384 GB |
| 5 | NVIDIA RTX 6000 ADA | 48 GB | 4x / 8x | 192 GB / 384 GB |
| 6 | NVIDIA RTX Pro 6000 Blackwell | 96 GB | 4x / 8x | 384 GB / 768 GB |
Embedded Inference Hardware: Embedded GPU
The setup includes a dedicated embedded GPU hardware platform that enables learners to understand and practice the process of deploying and inferring trained models/networks on embedded systems.
This hardware is bundled with a customized suite of tools and libraries, conveniently provided on an external hard drive/SSD, ensuring a smooth and efficient learning experience.
Research Areas:
- ✓ Machine Vision
- ✓ Robotics
- ✓ Deep Learning Model Inference
- ✓ Machine Learning
- ✓ Medical Imaging
- ✓ Gaming
- ✓ Virtual Reality
- ✓ NLP
- ✓ And Many More...
Embedded GPU with 6 core NVIDIA
Technical Specifications:
- 6-core NVIDIA ARMv8.2 CPU
- 384-core Volta GPU @ 1100MHz with 64 Tensor Cores
- Dual Deep Learning Accelerator (DLA) engines
- 7-way VLIW Vision Accelerator
- 8GB LPDDR4x
- MIPI CSI-2 lanes
- UART, SPI, I2C, I2S, CAN, GPIOs
Embedded GPU with 12 core NVIDIA
Technical Specifications:
- 12-core NVIDIA ARMv8.2 CPU
- 2048-core Ampere GPU @ 1.3 GHz with 64 Tensor Cores
- Dual Deep Learning Accelerator (DLA) engines
- Programmable Vision Accelerator
- 64GB LPDDR5
- 64GB eMMC
- UART, SPI, I2C, I2S, CAN, GPIOs
Camera Setup
This hardware setup includes a range of camera systems with supporting accessories, designed to facilitate the implementation of AI skills in the field of computer vision and image processing. The setup is bundled with multiple types of cameras, as outlined below:
- Thermal Camera – For infrared imaging and heat signature analysis
- 3D-Stereo Camera – For depth estimation and 3D vision applications
- Night Vision Camera – For low-light and dark-environment imaging
- IP Camera (Wireless) – For network-based vision applications
- USB Camera – For standard imaging and prototyping
Robotic Setup
This hardware setup includes a Robotic Arm and Robotic Car, which can be utilized to implement AI skills through the Embedded GPU Kit and the Robotic Operating System (ROS).
The platform enables learners to design, build, and test AI-driven robotic applications, offering hands-on experience in areas such as autonomous navigation, robotic control, and intelligent automation.
Robotic Arm with Embedded GPU
This hardware setup includes Robotic Arm which can used to implement AI skills using Embedded GPU Kit & Robotic Operating System (ROS) by building applications.
This hardware setup includes Robotic Arm which can used to implement AI skills using Embedded GPU Kit & Robotic Operating System (ROS) by building applications.
AI / ML Based Jetson Nano AI Robotic Car
Applications:
- AI racing car powered by NVIDIA Jetson Nano
- DonKeyCar Project: DonKeyCar utilizes deep learning neural network framework Keras/TensorFlow, together with computer vision library OpenCV, to achieve self driving
- SLAM Lidar Mapping: Mapping with odometer, IMU, lidar, EKF, etc.
AI/ML LAB SOFTWARE SETUP
The setup is pre-configured with Ubuntu OS (16.04 / 18.04) and comes with a comprehensive collection of libraries, utilities, tools, SDKs, and datasets, ensuring a ready-to-use platform for AI and Robotics development.
Essential Utilities
CUDA, cuDNN, TensorRT
Machine Learning Libraries
- Vowpal Wabbit, XGBoost
- NumPy, Scikit-learn, Pandas, and other essential Python libraries
Deep Learning Frameworks
- NVIDIA DIGITS
- TensorFlow
- Caffe, Caffe2
- PyTorch, Torch
- Theano
Pre-loaded Datasets
ImageNet, CIFAR-10, KITTI (ready for out-of-the-box development and experimentation)
Robotics Tools & Frameworks
- ROS (Robotic Operating System)
- OpenAI (Reinforcement Learning & Q-Learning)
- Simulation and visualization tools: Gazebo, RViz, MoveIt!, Autoware.ai, Apollo
Pitsco TETRIX PRIME Robot for FPGA Based Reconfigurable I/O(RIO) device:
The TETRIX Prime Robot, when combined with NI FPGA Based RIO device provides a powerful and flexible learning platform for exploring robotics, mechatronics, and control systems. Built on a durable and modular construction system, the TETRIX Prime kit allows students to quickly design and assemble a variety of robotic models. With NI RIO's real-time processing, I/O capabilities, and LabVIEW integration, students can program and control their robots with industry-standard tools.
ROVER VEHICLE ASSEMBLY
Students are tasked with building a robot that can drive, turn and process commands from a computer via Wi-Fi. Students will later add more advanced functionality such autonomous operation. This first project is a versatile and fun starting point for a variety of mechatronics design projects.
BALANCING ARM ASSEMBLY
The TETRIX Prime Balancing Arm is an assembly that demonstrates how control concepts taught in engineering classes can be applied. Students will learn how to integrate sensors, servos and a PID control algorithm with a robot that balances a ball in a position specified by the user.
SELF BALANCING ROBOT
The self-balancing robot is a complex closed-loop control system that autonomously balances itself in place. Students build the mechanical structure, create a system that read multiple sensor inputs, and implement a PD control algorithm to keep the robot upright.
Harness the power of real-time intelligence, reliability, and flexibility with our FPGA-based IIoT solutions. Designed for modern industries, our offering enables high-speed data acquisition, seamless connectivity, and smarter decision-making through a complete hardware-software ecosystem.
Our Solution Package Includes
FPGA-Based Reconfigurable I/O Controller & Chassis - High-performance, flexible control system with modular I/O for diverse industrial needs.
LabVIEW Software with Real-Time & FPGA Module - Powerful programming and monitoring environment for data processing, analysis, and rapid system deployment.
ThingsSpotIIoT Toolkit - Ready-to-use IIoT integration toolkit for secure cloud connectivity, analytics, and remote monitoring.
Essential Sensor Set - Reliable basic sensors to enable accurate data acquisition and smart industrial applications.
IIoT Software Module
The Things SpotIIoT Toolkit is a software add-on for LabVIEW that provides an Industrial Internet of Things (IIoT/IoT) solution to help you control and gather data from industrial controllers, programmable logic controllers (PLCs), sensors, and IoT devices.
The add-on supports cross-integration with NI embedded controllers including CompactRIO Controllers and CompactRIO Single-Board Controllers.
You can log data to local databases and view data visualized on configurable dashboards that offer real-time control and monitoring through a web GUI.
With the ThingsSpotIIoT Toolkit, you can also push the data to cloud services such as IBM Cloud, Microsoft Azure, Amazon Web Services (AWS), and more.
Conclusion
The AI/ML-Based Robotics Lab serves as a powerful platform that fuses intelligent algorithms with real-world robotic systems. By enabling smart automation, adaptive control, and vision-based solutions, it transforms theoretical learning into practical innovation.
This lab not only fosters hands-on expertise in AI, ML, and Industry 4.0 but also inspires creativity, problem-solving, and future-ready engineering skills—empowering learners to become leaders in next-generation technology.
Choose us for Professionalism— Your Gateway to Advanced AI and Machine Learning