CMOS plus stochastic MTJ paper out in Nature Communications
Happy to share our work on CMOS + stochastic Magnetic Tunnel Junctions (sMTJ) for probabilistic inference & learning just published in Nature Communications.
We show:
Creatively integrating stochastic MTJs with CMOS circuits can
(a) enhance the quality of randomness found in inexpensive and compact random number generators,
(b) perform energy-efficient inference and training for deep generative energy-based models in variation-tolerant asynchronous architectures,
(c) displace more than 10,000 digital transistors per building block.
This work was led by the talented Nihal Singh and in collaboration with Prof.'s Shunsuke Fukami and Hideo Ohno.
Our table-top demonstration proves that CMOS + X can be greater than CMOS alone and it is a stepping stone for bigger and better models.
