Ultra Low Energy Computer Memory for AI Learning in Medical and Other Applications | NextBigFuture.com

The CEA-Leti-based team’s uses machine learning on RRAM to get 100,000 times less energy usage. They use randomness instead of trying to prevent it. This allows in-situ learning to be realized in a highly efficient fashion through the application of nanosecond voltage pulses to nanoscale memory devices. Compared to a CMOS implementation of its algorithm, the approach requires five orders of magnitude less energy. That is the rough equivalence of the difference in height between the tallest building in the world and a coin lying on the ground. RRAM has been applied to in-memory implementations of backpropagation algorithms to implement The post Ultra Low Energy Computer Memory for AI Learning in Medical and Other Applications first appeared on NextBigFuture.com.