Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-level Binarized LSTM in EEG Classification for Wearable Devices   Long Short-Term Memory (LSTM) is widely used in various sequential
applications. Complex LSTMs could be hardly deployed on wearable and
resourced-limited devices due to the huge amount of computations and memory
requirements. Binary LSTMs are introduced to cope with this problem, however,
they lead to significant accuracy loss in some application such as EEG
classification which is essential to be deployed in wearable devices. In this
paper, we propose an efficient multi-level binarized LSTM which has
significantly reduced computations whereas ensuring an accuracy pretty close to
full precision LSTM. By deploying 5-level binarized weights and inputs, our
method reduces area and delay of MAC operation about 31* and 27* in 65nm
technology, respectively with less than 0.01% accuracy loss. In contrast to
many compute-intensive deep-learning approaches, the proposed algorithm is
lightweight, and therefore, brings performance efficiency with accurate
LSTM-based EEG classification to real-time wearable devices.

20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph