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KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
Scaling Context Requires Rethinking Attention
Carles Gelada, Jacob Buckman, Sean Zhang +1
We argue that neither transformers nor sub-quadratic architectures are well suited to training at long sequence lengths: the cost of processing the context is too expensive in the…
Conformal Transformations for Symmetric Power Transformers
Saurabh Kumar, Jacob Buckman, Carles Gelada +1
Transformers with linear attention offer significant computational advantages over softmax-based transformers but often suffer from degraded performance. The symmetric power (sympo…