Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM

Understand

Many long short-term memory (LSTM) applications need fast yet compact models.

• Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promising for cutting down network complexity by skipping insignificant weights.
• However, current compression strategies are mostly hardware-agnostic and network complexity reduction does not always translate into execution efficiency.
• In this work, we propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models.