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Reading barely 20% of the KV cache is enough to match up to 99% of dense attention accuracy at 32K context, yielding an immediate 1.69× speedup over FlashAttention without any retraining.
A million p-bits in a single programmable architecture reveals a universal tradeoff between throughput and accuracy in distributed probabilistic computing.
Attention bottlenecks in long-context decoding? SANTA slashes memory bandwidth demands by stochastically sampling value vectors, achieving 1.5x speedups without sacrificing accuracy.
Correlated diffusion, enabled by probabilistic computers, surpasses independent diffusion in generative modeling by exploiting structured probabilistic sampling.
Variational learning can tame the inherent chaos of nanoscale devices, paving the way for practical, larger-scale probabilistic computers.
