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Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning
Stef Cuyckens, Xiaoling Yi, Nitish Satya Murthy, Chao Fang, Marian Verhelst
https://arxiv.org/abs/2505.22404

Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning
Autonomous robots require efficient on-device learning to adapt to new environments without cloud dependency. For this edge training, Microscaling (MX) data types offer a promising solution by combining integer and floating-point representations with shared exponents, reducing energy consumption while maintaining accuracy. However, the state-of-the-art continuous learning processor, namely Dacapo, faces limitations with its MXINT-only support and inefficient vector-based grouping during backpro…