An energy efficient approximate adder with carry skip for error resilient neuromorphic VLSI systems

We propose a novel approximate adder design to significantly reduce energy consumption with a very moderate error rate. The significantly improved error rate and critical path delay stem from the employed carry prediction technique that leverages the information from less significant input bits in a parallel manner. An error magnitude reduction scheme is proposed to further reduce amount of error once detected with low cost. Implemented in a commercial 90 nm CMOS process, it is shown that the proposed adder is up to 2.4× faster and 43% more energy efficient over traditional adders while having an error rate of only 0.18%. The proposed adder has been adopted in a VLSI-based neuromorphic character recognition chip using unsupervised learning. The approximation errors of the proposed adder have been shown to have negligible impact on the training process. Moreover, the energy savings of up to 48.5% over traditional adders is achieved for the neuromorphic circuit with scaled supply level. Finally, we achieve error-free operations by including a low-overhead error correction logic.