This paper still existed some problems that I hope the authors could illustrate in a clearer way.
The authors argued that they were the first time to directly training deep SNNs with more than 100 layers. I don’t think this is the core contribution in this paper, because of the residual block, the spiking could be deeper. In my opinion, SEW structure is the most important point in this paper, and directly training a 50-layer and 100-layer snn is not a huge breakthrough. Otherwise, if they could give a more detailed analysis about why other methods can’t train a 100-layer snn except section 3.2, it may be more reasonable.
Why the RBA block can be seen as a special case of the SEW block? I mean SEW is another kind of RBA with binary input and output.
Equ. 11 is wonderful, how about other bit operations?
Fig. 5 a seems strange, please give more explanations.
When the input is aer format, how did you deal with DVS input?
If you can analyze the energy consumption as reference[15] did, this paper would be more solid.
11 is wonderful, how about other bit operations? Fig. 5 a seems strange, please give more explanations. When the input is aer format, how did you deal with DVS input? If you can analyze the energy consumption as reference[15] did, this paper would be more solid.