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cs.DC2026
FlashT2S: GPU-Accelerated Tensor-to-Spike Conversion Operators for Heterogeneous Neuromorphic LLM Backends
Rongyuan Guo, Haotian Wang, Yufeng Zhang +2
Artificial intelligence faces two growing challenges: computational bottlenecks and energy inefficiency inherent to the von Neumann architecture. Neuromorphic computing offers a pr…
cs.DC2026
A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs
Chen Zhang, Yan Ding, Haotian Wang +3
During the deployment of Large Language Models (LLMs), the autoregressive decoding phase on heterogeneous NPU platforms (e.g., Ascend 910B) faces severe memory-bound challenges. Th…
cs.DC2024
BLITZSCALE: Fast and Live Large Model Autoscaling with O(1) Host Caching
Dingyan Zhang, Haotian Wang, Yang Liu +4
Model autoscaling is the key mechanism to achieve serverless model-as-a-service, but it faces a fundamental trade-off between scaling speed and storage/memory usage to cache parame…