1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.RO2026
ActionCache: Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
Ryuji Oi, Hikari Otsuka, Kosuke Matsushima +4
Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow-matching-based VLA models have shown remarkabl…
cs.AR2026★ 1 cited
AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization
Kosuke Matsushima, Yasuyuki Okoshi, Masato Motomura +1
Processing-in-Memory (PIM) architectures offer a promising solution to the memory bottlenecks in data-intensive machine learning, yet often overlook the growing challenge of activa…