Why your DL accelerator should be replaced
Intelligence is quickly being added to all our electronics devices. Whether it’s our vehicles that automatically brake when things get dangerous, our phone’s cameras that ensure every picture we take looks great, or our datacenters that need to not just store and distribute our videos, but also understand what’s in them – intelligent image processing using deep learning is everywhere.
But just adding a deep learning accelerator next to a chip’s host CPU subsystem doesn’t mean you have a chip that can handle all the required visual computing tasks. While we’ve seen some designs that didn’t realize this, many SOCs these days do have multiple compute engines for the different imaging-related processing duties.
To read the full article, click here
Related Blogs
- Why You Should Create Your Own NPU Benchmarks
- Why You Can't Trust Your NPU Vendor's Benchmarks
- Why Secure Boot is Your Network’s Best Friend (And What BlackTech Taught Us)
- Mentor Graphics Should Be Acquired or Sold: Carl Icahn
Latest Blogs
- Why Hardware Monitoring Needs Infrastructure, Not Just Sensors
- Why Post-Quantum Cryptography Doesn’t Replace Classical Cryptography
- The Silent Guardian of AI Compute - PUFrt Unifies Hardware Security and Memory Repair to Build the Trust Foundation for AI Factories
- Heterogeneous NPU Data Movement Tax: Intel's Own Slides Tell the Story
- PQMicroLib-Core now supports PSA Certified Crypto API