February 22, 2024
Sree Ganesan, VP of Product at d-Matrix, discusses the limitations of traditional architectures when it comes to energy-efficient AI inference and how in-memory computing is emerging as a promising alternative.
Given the rapid pace of adoption of generative AI, it only makes sense to pursue a new approach to reduce cost and power consumption by bringing compute in memory and improving performance. By flipping the script and reducing unnecessary data movement, we can make dramatic improvements in AI efficiency and improve the economics for AI going forward
Read the full article on insideHPC
