Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Power-Efficient Binary-Weight Spiking Neural Network Architecture for
  Real-Time Object Classification   Neural network hardware is considered an essential part of future edge
devices. In this paper, we propose a binary-weight spiking neural network
(BW-SNN) hardware architecture for low-power real-time object classification on
edge platforms. This design stores a full neural network on-chip, and hence
requires no off-chip bandwidth. The proposed systolic array maximizes data
reuse for a typical convolutional layer. A 5-layer convolutional BW-SNN
hardware is implemented in 90nm CMOS. Compared with state-of-the-art designs,
the area cost and energy per classification are reduced by 7$\times$ and
23$\times$, respectively, while also achieving a higher accuracy on the MNIST
benchmark. This is also a pioneering SNN hardware architecture that supports
advanced CNN architectures.

12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat