TABLE II:

Comparison of Machine Learning SoCs

Parameter	TCAS-H’21 [18]	JETCAS’19 [101]	CICC’20 [100]
Process	180 nm	28 nm	180 nm
Classifier	Multi-ANN+	CNN	DNN
Application	Migraine Detection	Emotion Detection	Emotion Detection
Features	HFO, BPF, Peak latency	Off-chip	ZCD, SK
Signal Modality	SEP	EEG	EEG
Closed-loop	N	N	N
# of Sensing Channels		6	2
ML Energy Efficiency	N.A.	N.A.	10.13 μJ/class.
ML Power	249 μW	76.61 mW	N.A.
Total Area (ML Area)	0.5 (0.5) mm2	3.35 (3.35) mm2	16 (6.02*) mm2
Sampling Rate/Ch.	5 kS/s	250 S/s	N.A.
Accuracy	76%	83.4%**	85.2%
Dataset (# patients)	MI, MII (42), HV (15)	DEAP (32)	DEAP (32), SEED
Latency	50 ms†	0.45 s†	<1min†
ML Energy/Ch.	49.8 nJ/S	51.1 μJ/S	N.A.
ML Area/Ch.	0.5 mm2	0.558 mm2	3.01 mm2
ML E-A FoM	24.9 nJ·mm2/S	29 μJ·mm2/S	N.A.

⁺Post place-and-route results.

^*ML (feature extractor and classifier) area estimated from chip micrograph

^**Accuracy metric

^†Processing (system) latency