Name
Abstract | ||
|---|---|---|
Recurrent Neural Networks (RNN) can be large and compute-intensive, making them hard to deploy on resource constrained devices. As a result, there is a need for compression technique that can significantly compress recurrent neural networks, without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker products. We call the RNNs compressed using Kronecker products as Kronecker product Recurrent Neural Networks (KPRNNs). KPRNNs can compress the LSTM[22], GRU [9] and parameter optimized FastRNN [30] layers by 15 - 38x with minor loss in accuracy and can act as in-place replacement of most RNN cells in existing applications. By quantizing the Kronecker compressed networks to 8 bits, we further push the compression factor to 50x. We compare the accuracy and runtime of KPRNNs with other state-of-the-art compression techniques across 5 benchmarks spanning 3 different applications, showing its generality. Additionally, we show how to control the compression factors achieved by Kronecker products using a novel hybrid decomposition technique. We call the RNN cells compressed using Kronecker products with this control mechanism as hybrid Kronecker product RNNs (HKPRNN). Using HKPRNN, we compress RNN Cells in 2 benchmarks by 10x and 20x achieving better accuracy than other state-of-the-art compression techniques. |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.02876 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Urmish Thakker | 1 | 1 | 3.74 |
| Jesse G. Beu | 2 | 2 | 3.41 |
| Dibakar Gope | 3 | 10 | 3.29 |
| Chu Zhou | 4 | 0 | 1.01 |
| Igor Fedorov | 5 | 12 | 6.07 |
| Ganesh S. Dasika | 6 | 387 | 24.30 |
| Matthew Mattina | 7 | 441 | 28.63 |