Neural Networks and the Future
The recent embedded neural network symposium held at Cadence wrapped up with a panel session. Chris Rowen was the moderator and I think the panelists were Han Song, Ren Wu, Forest Iandola, Kai Yu and Jeff Bier (who all presented earlier). I didn't really note down who said what so I'll just report on some of the points that were made. Stuff in [square brackets] are my additional comments, not something any of the panelists said explicitly.
During the sessions, several speakers talked about how 8 bits (or even 4 bits or, in some cases, 2) are precise enough, and 32-bit floating point isn't really needed. But all of the real-world applications seem to be sticking with GPUs. The panelists figured that it was the lack of experience, it is only just showing up in the literature now. Everyone is excited by how fast the field is moving but the approaches actually being deployed are changing much more slowly. It only takes one highly visible success to move people, but going from 0 to 1 is really hard.
To read the full article, click here
Related Blogs
- Flash Forward: MRAM and RRAM Bring Embedded Memory and Applications into the Future
- The Future of Technology: Transforming Industrial IoT with Edge AI and AR
- Ethernet Evolution: Trends, Challenges, and the Future of Interoperability
- Deep Robotics and Arm Power the Future of Autonomous Mobility
Latest Blogs
- The Hidden Threat in Analog IC Migration: Why Electromigration rules can make or break your next tapeout
- MIPI CCI over I3C: Faster Camera Control for SoC Architects
- aTENNuate: Real-Time Audio Denoising
- From guesswork to guidance: Mastering processor co-design with Codasip Exploration Framework
- Enabling AI Innovation at The Far Edge