  There is increasing demand to bring machine learning capabilities to low
power devices. By integrating the computational power of machine learning with
the deployment capabilities of low power devices, a number of new applications
become possible. In some applications, such devices will not even have a
battery, and must rely solely on energy harvesting techniques. This puts
extreme constraints on the hardware, which must be energy efficient and capable
of tolerating interruptions due to power outages. Here, as a representative
example, we propose an in-memory support vector machine learning accelerator
utilizing non-volatile spintronic memory. The combination of
processing-in-memory and non-volatility provides a key advantage in that
progress is effectively saved after every operation. This enables instant shut
down and restart capabilities with minimal overhead. Additionally, the
operations are highly energy efficient leading to low power consumption.
