Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Machine Learning Accelerator In-Memory for Energy Harvesting   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.

3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc