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Critical visual token selection is driven by a select few attention heads, and leveraging this insight can drastically improve VLM efficiency without sacrificing performance.
Achieving 205脳 latency reduction in FPGA-based neural networks could redefine the benchmarks for ultra-fast inference in latency-critical applications.
HiFuzz outperforms traditional fuzzing techniques by leveraging hierarchical reinforcement learning to achieve deeper architectural state exploration and improved bug detection.
CGRA performance jumps by 2.7x thanks to NEURA, a compilation framework that elegantly transforms control flow into dataflow.
