  One of the most exciting applications of Spin Torque Magnetoresistive Random
Access Memory (ST-MRAM) is the in-memory implementation of deep neural
networks, which could allow improving the energy efficiency of Artificial
Intelligence by orders of magnitude with regards to its implementation on
computers and graphics cards. In particular, ST-MRAM could be ideal for
implementing Binarized Neural Networks (BNNs), a type of deep neural networks
discovered in 2016, which can achieve state-of-the-art performance with a
highly reduced memory footprint with regards to conventional artificial
intelligence approaches. The challenge of ST-MRAM, however, is that it is prone
to write errors and usually requires the use of error correction. In this work,
we show that these bit errors can be tolerated by BNNs to an outstanding level,
based on examples of image recognition tasks (MNIST, CIFAR-10 and ImageNet):
bit error rates of ST-MRAM up to 0.1% have little impact on recognition
accuracy. The requirements for ST-MRAM are therefore considerably relaxed for
BNNs with regards to traditional applications. By consequence, we show that for
BNNs, ST-MRAMs can be programmed with weak (low-energy) programming conditions,
without error correcting codes. We show that this result can allow the use of
low energy and low area ST-MRAM cells, and show that the energy savings at the
system level can reach a factor two.
