AI Core Station ^ AI Accelerators and a naive guide to AI hardware:

Here is a small guide I wrote while building my lab's AI workstation @ DigiLab, LSBG and relevant information I learned while building the system are documented. I will cover 5 topics in this blog and here is an overview diagram of the workstation:

Topics:

• AI/ ML / DL Workstation Build
• Robotics, Electronics and Hardware
• AI Accelerators
• Benchmarking GPU Performance
• Benchmarking Standard DL tasks
  
  

#1 AI/ ML / DL Workstation Build

• Essence of Computation & Networking NOTES
• AI / ML / DL hardware core components NOTES
• Parallel Programming with CUDA NOTES
• Final AI Work Station build NOTES

#2 Robotics, Electronics and Hardware

• Electronics components & workbench NOTES
• Robotics Components NOTES
• Robotics and Mechatronics NOTES
• IoT & AI on Edge devices NOTES
• 3D printing & PCB Design NOTES
• DIY & Advanced Robotics hardware NOTES
• Robot Operating System [ROS] NOTES
• Autonomous Systems NOTES

#3 AI Accelerators

• AI accelerators NOTES
• Future Computing NOTES
• Mobile AI workstations/ laptops NOTES

#4 Benchmarking System Performance

To know more about linux : you might find useful content in my repository/@linux_d

• 3D Mark Benchmark [x report] as no test suite for linux but windows
• Phoronix test suite report ✅✅
• Paessler Network benchmark link only windows sys (for linux - phoronix/ethr or phoronix/speedtest-cli)
• LAMMPS benckmark link ( classical molecular dynamics in phoronix )
• VASP benchmark link ( atomic scale molecular modelling in phoronix)
• V-Ray 5 benchmark link ( render latency benchmark in phoronix)

#5 Benchmarking Standard DL tasks

To know more about AI : you might find useful content in my repository/@aicore_n

• AI environment setup with CUDA, cudnn and python3 in linux report
• Stanford DAWN Deep Learning Benchmark [x report] - retired benchmark and upgraded to mlperf
• MLPerf training benchmarks report
• AI-Benchmark Alpha / Burnout report

@lab : lab_video_draft_22"