Neural networks (NNs) have emerged as an effective equalization approach for high-speed intensity modulation and direct detection (IM/DD) optical links, where chromatic dispersion, limited bandwidth, and device-induced nonlinearities degrade signal quality. However, the heavy reliance on multiplications in conventional NNs leads to high computational complexity, limiting hardware deployment. Addition-based convolutional NN (AdderCNN) was originally developed for image classification, where it reduces complexity by replacing multiplications with subtractions and accumulations. In this work, we propose a multiplier-free AdderCNN equalizer for up to 200 Gbaud on-off keying IM/DD links using a 26 GHz O-band ring resonator modulator. After 500 m single-mode fiber transmission, AdderCNN achieves bit error rate (BER) below the 7% overhead (OH) hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10^-3, outperforming conventional equalizers. Compared to classical CNN, AdderCNN eliminates all real multiplications (RM), reduces bit operations (BOP) by 87.6% and the number of adders and shifters (NABS) by 96.9% per equalized symbol under 32-bit full-precision with comparable performance. With 7-bit quantization, it further reduces BOP by 72.3% and NABS by 71.0% per equalized symbol, while keeping BER below the 7% OH HD-FEC threshold. Therefore, AdderCNN has the potential to become a hardware-efficient NN equalization solution for next-generation optical interconnects.