“2013-07-22”版本间的差异

2013年7月22日 (一) 12:30的版本

Data sharing

• LM count files still undelivered!

DNN progress

Experiments

• Sparse DNN.

1200-1200-1200-3536 1200-1200-1200-3536-sparse0.3 (sparsity 1/5)

original atlas: RT 2.3 RT 2.3

atlas sparse: RT 54 RT 14

NIST smatmat: RT 27.3 RT 5.98

800-800-800-2108 800-800-800-2108-sparse0.3 (sparsity 2/5):

original atlas: RT 1.1 RT 1.1

NIST smatmat: RT 11.9 RT 5.5

Conclusions:

1. the atlas works well for both non-sparse and sparse.
2. sparsity does not work if the sparsity rate is low. It looks the sparsity computing can

outperform the non-sparsity computing only if the sparsity rate is higher than 1/15.

1. In another words, to employ sparsity, the cost that first should be taken is the error rate

increase with the 1/15 compression.

1. The sparse approach seems more useful for storage: if the sparsity is higher than 1/2, then the

storage of CSR/CSC will start to save storage.

1. Possibly unit-based sparsity instead of weight sparsity.

Tencent exps

GPU & CPU merge

1. Hold

Embedded progress

• Tested various PS models:

ID            model        feature    WER       RT          storage

semi_10000    semi HMM    s2-4x    6.30%       0.80          10.2M
semi_5000     semi HMM    s2-4x    6.70%       0.74          5.2M
semi_5000     semi HMM    1c-d-dd  9.11%       0.91          1.3M
ptm_5000      PTM HMM     s2-4x    6.47%       2.15          1.3M

So there is not a perfect which wins in terms all the criteria. Looks like semi-5000 is an acceptable trade-off.