Classification on SHD

96.3Accuracy

PfA SNN

Updated 1mo ago

Evaluation Results

Method	Links
PfA SNN 2026.02		96.3
Event-SSM 2026.02		95.9
SE-adLIF 2026.02		95.8
DCLS-Delays 2026.02		95.1
Bullet Trains 2026.02		95.1
2F256HD (Hammouamri et al.) 2026.02		95.07
Bullet Trains 2026.02		94.78
Temporal Skips with delay learning 2026.02		94.7
Recurrent SNN 2026.02		94.6
Spectro-temporal graph on SoC FPGA 2026.02		94.5
Spectro-temporal graph 2026.02		94.3
Yang+ 2026.03		94
2F512HD (Mészáros et al.) 2026.02		93.1
DL128-SNN-Dloss 2026.02		92.6
STSC 2022.10		92.4
STSC-SNN 2026.02		92.4
S5-RF 2026.02		91.9
TA-SNN 2026.02		91.1
TA-SNN 2022.10		91.08
TA-SNN 2021.07		91.08
Adaptive SRNN 2022.10		90.4
Adaptive SRNN 2026.02		90.4
Spectro-temporal graph 2026.02		90
RELU SRNN 2021.07		88.93
Spectro-temporal graph on SoC FPGA 2026.02		88.8
Cuba-LIF 2026.02		87.8
Fully configurable FPGA SNN 2026.02		87.8
FF SNN with dendritic delays 2026.02		87.6
EventProp-GeNN 2026.02		84.8
SG-based SNN 2021.07		84
Hetero. RSNN 2022.10		83.5
Convolutional RSNN 2026.02		83.5
Recurrent SNN 2026.02		83.2
LIF RSNN 2022.10		73.3
RSNN on FPGA 2026.02		72.9
LIF RSNN 2021.07		71.4