Research Sites/Labs
SRM University-AP is home to a number of advanced research sites and labs designed to support groundbreaking scientific exploration and innovation. These state-of-the-art facilities provide students and faculty with the tools and technologies needed to push the boundaries of knowledge in various fields. Key research sites include the Artificial Intelligence Lab, Biotechnology Lab, Materials Science Lab, and Renewable Energy Lab. These labs foster interdisciplinary collaboration, encouraging innovative solutions to real-world problems.
Academic Labs
Eleven Academic Labs with 65 systems each
Six Specialisation labs (getting established
Six Specialisation labs (getting established
- Cybersecurity Lab with 60 systems
- Network Lab with 60 systems
- IoT Lab with 60 systems (and other equipments)
- Data Science and AIML Lab with 140 systems
- Distributed and Cloud computing Lab with 60 systems
17
Labs
1095
Systems
System Specification
| Types of Systems | Total | Lab location |
|---|---|---|
| Intel i7 with GPU (NVIDIA GeForce RTX 3060 with 12GB 3584 NVIDIA CUDA® Cores) 32GB DDR5, 1TB SSD |
244 | New Academic Building |
| Intel i5 10400 2.9GHz ,16 GB, 500 GB HDD Windows 11 with Intel Wifi pro |
782 | W101,W106,W403,W601,W62, W603,W604 Vikram Sarabhai Block, Labs in: New Academic building |
| i7,16 GB DDR4 2933 DIMM, 1TB SSD with Intel Wifi pro | 70 | W102, Vikram Sarabhai Block |
| DGX NVIDIA with GPU 32 GB | 01 | Data Centre |
Research Labs
- DGX
The NVIDIA DGX-1 is a deep learning system, architected for high throughput and high interconnect bandwidth to maximize neural network training performance. The core of the system is a complex of Eight Tesla V100 GPUs connected in the hybrid cube-mesh NVLink network topology.
In addition to the eight GPUs, DGX-1 includes two CPUs for boot, storage management, and deep learning framework coordination. DGX-1 is built into a three-rack-unit (3U) enclosure that provides power, cooling, network, multi-system interconnect, and SSD file system cache, balanced to optimize throughput and deep learning training time.
Hardware Overview
- GPUs 8xTesla V100
- GPU Memory 256 GB (32 GB/GPU)
- CPU Dual 20-core Intel Xeon E5-2698 v4 2.2 GHz
- NVIDIA CUDA Cores 40,960
- NVIDIA Tensor Cores (on V100-based systems) 5,120
- System Memory 512 GB 2.133 GHz DDR4 RDIMM
- Storage 4x1.92 TB SSD RAID-0
- Network Dual 10 GbE
Performance - 1 PETA FLOPS [Mixed Precision]
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- Cloud Computing Lab
Multi-node Openstack software-based private Cloud Lab consists of different nodes such as the Controller node, Compute node, Network node, and Storage node. This Lab is useful for conducting hands-on experience on a private cloud as well as also useful to do research work.
| Number of Systems | Software Specification | Hardware Specification |
|---|---|---|
| 6 (i7 Systems) | Openstack Cloud (Multi-node), Ubuntu 20 |
Intel i7-10700 CPU @ 2.9 GHZ X 16 RAM: 16 GB HDD: 1 TB Graphics: Mesa Intel @ UHD Graphics 630 |
