Quality follows upgrading
Tag: cs.AR
arXiv:2604.02638v1 Announce Type: new Abstract: We propose AXELRAM, a smart SRAM macro architecture that computes attention scores directly from quantized KV cache indices without dequantization. …
arXiv:2604.02556v1 Announce Type: new Abstract: Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. …
arXiv:2603.14239v1 Announce Type: new Abstract: SystemVerilog Assertions (SVAs) are crucial for hardware verification. Recent studies leverage general-purpose LLMs to translate natural language properties to SVAs …
arXiv:2603.12269v1 Announce Type: cross Abstract: Early-exit deep neural networks enable adaptive inference by terminating computation when sufficient confidence is achieved, reducing cost for edge AI …
arXiv:2603.10026v1 Announce Type: cross Abstract: Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has …
arXiv:2603.10030v1 Announce Type: cross Abstract: AI transport libraries move bytes efficiently, but they commonly assume that buffers are already correctly allocated, placed, shared, registered, and …
arXiv:2603.10100v1 Announce Type: new Abstract: Modern CNNs' high computational demands hinder edge deployment, as traditional ``hard'' sparsity (skipping mathematical zeros) loses effectiveness in deep layers …
arXiv:2603.08715v1 Announce Type: cross Abstract: Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between model characteristics …
arXiv:2603.08960v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models deliver high quality at low training FLOPs, but this efficiency often vanishes at inference. We identify a …
arXiv:2603.09032v1 Announce Type: new Abstract: Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict …
arXiv:2603.09161v1 Announce Type: new Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual …
arXiv:2603.09274v1 Announce Type: new Abstract: Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used …