Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model
  Compression   Even though fine-grained pruning techniques achieve a high compression ratio,
conventional sparsity representations (such as CSR) associated with irregular
sparsity degrade parallelism significantly. Practical pruning methods, thus,
usually lower pruning rates (by structured pruning) to improve parallelism. In
this paper, we study fixed-to-fixed (lossless) encoding architecture/algorithm
to support fine-grained pruning methods such that sparse neural networks can be
stored in a highly regular structure. We first estimate the maximum compression
ratio of encoding-based compression using entropy. Then, as an effort to push
the compression ratio to the theoretical maximum (by entropy), we propose a
sequential fixed-to-fixed encoding scheme. We demonstrate that our proposed
compression scheme achieves almost the maximum compression ratio for the
Transformer and ResNet-50 pruned by various fine-grained pruning methods.

11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an