Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synapse Compression for Event-Based Convolutional-Neural-Network
  Accelerators   Manufacturing-viable neuromorphic chips require novel computer architectures
to achieve the massively parallel and efficient information processing the
brain supports so effortlessly. Emerging event-based architectures are making
this dream a reality. However, the large memory requirements for synaptic
connectivity are a showstopper for the execution of modern convolutional neural
networks (CNNs) on massively parallel, event-based (spiking) architectures.
This work overcomes this roadblock by contributing a lightweight hardware
scheme to compress the synaptic memory requirements by several thousand times,
enabling the execution of complex CNNs on a single chip of small form factor. A
silicon implementation in a 12-nm technology shows that the technique increases
the system's implementation cost by only 2%, despite achieving a total
memory-footprint reduction of up to 374x compared to the best previously
published technique.

9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio