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Sparse attention reduces core-attention computation, but its indexers still incur repeated selection work and per-layer key-cache storage. Reusing selected indices across layers reduces this overhead but constrains multiple layers to the same token set. We introduce LatentIndex, which extends the latent-sharing princip...
Polymers are fundamental to modern materials science because their backbone chemistry, monomer composition, and chain architecture can be systematically tuned to achieve a virtually unlimited range of mechanical, thermal, electronic, and optical properties. To accelerate the discovery and design of polymeric material...
A workload characterization study of 13 419 CNN configurations on two GPU platforms under GPU telemetry reveals that energy, latency, and memory exhibit fundamentally distinct scaling behaviors: energy and latency diverge by 3x under high computational demand, and cross-GPU transferability differs by target.
This work proposes HISA (Hierarchical Indexed Sparse Attention), a plug-and-play replacement for the indexer that rewrites the search path from a flat token scan into a two-stage hierarchical procedure: a block-level coarse filtering stage that scores pooled block representations to discard irrelevant regions, followed...
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