Recurrent neural networks (RNNs) have shown excellent performance in
processing sequence data. However, they are both complex and memory intensive
due to their recursive nature. These limitations make RNNs difficult to embed
on mobile devices requiring real-time processes with limited hardware
resources. To address the above issues, we introduce a method that can learn
binary and ternary weights during the training phase to facilitate hardware
implementations of RNNs. As a result, using this approach replaces all
multiply-accumulate operations by simple accumulations, bringing significant
benefits to custom hardware in terms of silicon area and power consumption. On
the software side, we evaluate the performance (in terms of accuracy) of our
method using long short-term memories (LSTMs) on various sequential models
including sequence classification and language modeling. We demonstrate that
our method achieves competitive results on the aforementioned tasks while using
binary/ternary weights during the runtime. On the hardware side, we present
custom hardware for accelerating the recurrent computations of LSTMs with
binary/ternary weights. Ultimately, we show that LSTMs with binary/ternary
weights can achieve up to 12x memory saving and 10x inference speedup compared
to the full-precision implementation on an ASIC platform.

该论文提出了一种在训练阶段就能够学习二进制和三进制权重的方法，可以用于硬件实现循环神经网络，以达到减少硅面积和功耗的目的。使用该方法可以在运行时使用二进制 / 三进制权重，从而在 ASIC 平台上实现长短时记忆神经网络比全精度实现要高出 10 倍的推理加速度和 12 倍的内存节省。