The rise of machine learning is giving renewed life to compute-in-memory processors now in the works at startups, IBM, and universities.
SAN JOSE, Calif. — Startups, corporate giants, and academics are taking a fresh look at a decade-old processor architecture that may be just the thing ideal for machine learning. They believe that in-memory computing could power a new class of AI accelerators that could be 10,000 times faster than today’s GPUs.
The processors promise to extend chip performance at a time when CMOS scaling has slowed and deep-learning algorithms demanding dense multiply-accumulate arrays are gaining traction. The chips, still more than a year from commercial use, also could be vehicles for an emerging class of non-volatile memories.
Startup Mythic (Austin, Texas) aims to compute neural-network jobs inside a flash memory array, working in the analog domain to slash power consumption. It aims to have production silicon in late 2019, making it potentially one of the first to market of the new class of chips.