hardware-co-design

Recurrent Looped Transformer (RLT) is a novel architecture combining a causal encoder with a recurrent decoder to achieve latent reasoning with unbounded temporal depth, model-hardware co-design, and model-RL algorithm co-design.

This paper introduces TRAM, a method that jointly optimizes approximate multiplier structures and AI model parameters to reduce power consumption in AI accelerators while maintaining accuracy.