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Hardware-Efficient CNNs: Interleaved Approximate FP32 Multipliers for Kernel Computation
Bindu G Gowda (International Institute of Information Technology Bangalore), Yogesh Goyal (International Institute of Information Technology Bangalore), Yash Gupta (International Institute of Information Technology Bangalore), Madhav Rao (International Institute of Information Technology Bangalore)

Hardware-Efficient CNNs: Interleaved Approximate FP32 Multipliers for Kernel Computation
Single-precision floating point (FP32) data format, defined by the IEEE 754 standard, is widely employed in scientific computing, signal processing, and deep learning training, where precision is critical. However, FP32 multiplication is computationally expensive and requires complex hardware, especially for precisely handling mantissa multiplication. In practical applications like neural network inference, perfect accuracy is not always necessary, minor multiplication errors often have little …

VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
Yixiao Wang, Mingxiao Huo, Zhixuan Liang, Yushi Du, Lingfeng Sun, Haotian Lin, Jinghuan Shang, Chensheng Peng, Mohit Bansal, Mingyu Ding, Masayoshi Tomizuka
https://arxiv.org/abs/2510.05213

VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generality across tasks. Distilling multiple VFMs into a unified representation for policy can mitigate this limitation but often yields inflexible task-specific feature selection and requires costly full re-training to incorporate robot-domain knowledge. We propose VER, a Vision Expert transformer for Robot learning. During …

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