Table 1 Comparison of LSTM performance with previous works

From: Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks

Metric

Our KWS task

Our NLP task

Nature’2331

Nat. Elec.’2323

VLSI’1728

JSSC’2025

ISSCC’1726

CICC’1827

CMOS technology

16 nm

16 nm

14 nm

14 nm

65 nm

65 nm

65 nm

65 nm

Memory technology

RRAM

RRAM

PCM

PCM

SRAM

SRAM

SRAM

SRAM

Operation Frequency (MHz)

1000

1000

1000

1000

200

80

200

168

IMC

Y

Y

Y

Y

N

N

N

N

Input/weight/output precision

5(8)/Analog/5(8)

5(8)/Analog/5(8)

8/Analog/8

8/Analog/8

8/8/–

13/6/13

16/16/–

8/8/8

Memory size (kB)

1.125

623

4250

272

348

288

10

82

KWS task on GSCD (Accuracy %)

88.5 (12 classes)

–

86.1 (12 classes)

–

–

–

–

–

NLP task on PTB (BPC)

–

1.349

–

1.439

–

–

–

–

Area (mm2)

0.003

0.71

111.18

144

19

7.74

2.6

0.93

Power (mW)

3.7 (5b), 4.6(8b)

406.5 (5b),766.8(8b)

3450

3465

296

65

2.3

29.03

Peak Throughput (TOPS)

0.11(5b), 0.02(8b)

19.5(5b), 5.5(8b)

23.94

4.9

0.38

0.16/0.02

0.025

0.03

Energy Efficiency (TOPS/W)

31.0(5b), 4.0(8b)

47.9(5b), 7.2(8b)

6.94

1.96

1.28

2.45/8.93

1.1

1.11

Area Efficiency (TOPS/mm2)

39.48 (5b), 6.1(8b)

27.6 (5b), 7.8(8b)

0.17

0.32

0.02

0.02/0.0025

0.01

0.02

Normalized Area Efficiency (TOPS/mm2, 1 GHz, 16 nm)

39.48 (5b), 6.1(8b)

27.6 (5b), 7.8(8b)

0.22

0.32

1.6

4/0.5

0.8

1.92