Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

Understand

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.

• The recent pruning based work ESE suffers from degradation of performance/energy efficiency due to the irregular network structure after pruning.
• We propose block-circulant matrices for weight matrix representation in RNNs, thereby achieving simultaneous model compression and acceleration.
• We aim to implement RNNs in FPGA with highest performance and energy efficiency, with certain accuracy requirement (negligible accuracy degradation).