- The results/analysis albeit being detailed and comprehensive, only two relatively old and small models are evaluated. - Some of the comparison with other related works, is not completely apple-to-apples, for instance comparing fixed point representation for training, while comparing against AdderNet and DeepShift which use at least half floating points for training. Its understandable the fixed point representation has benefits, but it would probably have been more relevant to compare against similar such fixed point training, for instance other works such (not limited to) - https://arxiv.org/abs/1802.00930, https://arxiv.org/abs/1802.04680, https://arxiv.org/abs/1909.02384 - While the FPGA implementation and results with that are quite impressive and is very valuable as a prototype for the proposed method. However, for such HW solution it would help if the authors extend to do a wider comparison. For instance, with a dedicated ASIC-based implementation the quantum of benefits (table.2) would be considerably reduced. Since fp multiplication could still be cheaper since most optimizations would require non-trivial changes to the datapath, which would take away for the benefits of the faster computations
- The results/analysis albeit being detailed and comprehensive, only two relatively old and small models are evaluated.