Time-step interleaved weight reuse for LSTM neural network computing
Naebeom Park, Yulhwa Kim, Daehyun Ahn, Taesu Kim, Jae‐Joon Kim · 2020
In Long Short-Term Memory (LSTM) neural network models, a weight matrix tends to be repeatedly loaded from DRAM if the size of on-chip storage of the processor is not large enough to store the entire matrix. To alleviate heavy overhead of DRAM access for weight loading in LSTM computations, we propose a weight reuse scheme which utilizes the weight sharing characteristics in two adjacent time-step computations. Experimental results show that the proposed weight reuse scheme reduces the energy consumption by 28.4-57.3% and increases the overall throughput by 110.8% compared to the conventional schemes.