[Retracted] A Post-training Quantization Method for the Design of Fixed-Point-Based FPGA/ASIC Hardware Accelerators for LSTM/GRU Algorithms

Table 2

Comparison of quantization results on the IMDb dataset.

	Model	#Layers	#Units	Quantization method	Weights bits	Activation bits	FP model accuracy	Quantized model accuracy	Accuracy variation
[34]	LSTM	1	128	In-training	4	32	82.87	79.64	−3.23
[1]	LSTM	1	512	In-training	4	4	89.54	88.48	−1.06
[39]	LSTM	1	70	In-training	4	32	84.98	86.24	+1.26
[40]	LSTM	3	512	In-training	4	4	86.37	86.31	−0.06
Our work	LSTM	1	32	Post-training	5	14	89.19	88.86	−0.33
[34]	GRU	1	128	In-training	4	32	80.35	78.96	−1.39
[1]	GRU	1	512	In-training	4	4	90.54	88.25	−2.29
Our work	GRU	1	32	Post-training	5	20	90.24	90.23	−0.01