The team, led by Professor Yoo Hoi-jun at KAIST’s processing-in-memory research center, has developed what it says is the world’s first ‘Complementary-Transformer’ (C-Transformer) AI chip. This neuromorphic computing system mimics the structure and workings of the human brain, using a deep learning model often employed in visual data processing.
“Neuromorphic computing is a technology that even companies like IBM and Intel have not been able to implement, and we are proud to be the first in the world to run the LLM with a low-power neuromorphic accelerator,” Yoo said.
Questions remain
This technology learns context and meaning by tracking relationships within data, such as words in a sentence, which is a key technology for generative AI services like ChatGPT.
During a demonstration at the ICT ministry’s headquarters, team member Kim Sang-yeob showcased the chip’s capabilities. On a laptop equipped with the chip he performed tasks such as Q&A sessions, sentence summation, and translations using OpenAI’s LLM, GPT-2. The tasks were completed at least three times faster and, in some cases, up to nine times faster than when running GPT-2 on an internet-connected laptop.
Implementing LLMs in generative AI tasks typically requires numerous GPUs and 250 watts of power, but the team claims their semiconductor uses only 1/625 of the power of Nvidia’s GPU for the same tasks. In addition, because it is also 1/41 of the size, measuring just 4.5mm by 4.5mm, it could ultimately be used in devices like mobile phones.
Whether the chip can deliver on its promises in real-world applications remains to be seen, however. As Tom’s Hardware reports, “Though we are told that the KAIST C-Transformer chip can do the same LLM processing tasks as one of Nvidia’s beefy A100 GPUs, none of the press nor conference materials we have provided any direct comparative performance metrics. That’s a significant statistic, conspicuous by its absence, and the cynical would probably surmise that a performance comparison doesn’t do the C-Transformer any favors.“
