# Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction

## TL;DR

This paper demonstrates that Binarized Neural Networks can tolerate high bit error rates in Magnetoresistive RAM without error correction, enabling energy-efficient, low-area memory implementations for AI.

## Contribution

It shows that BNNs can operate reliably with uncorrected ST-MRAM errors up to 0.1%, relaxing design constraints and reducing energy consumption.

## Key findings

- Bit error rates up to 0.1% have minimal impact on BNN accuracy.
- Error correction is unnecessary for BNNs using ST-MRAM, simplifying design.
- Energy savings of up to 50% are achievable at the system level.

## Abstract

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.