1 citations · 1 across the 3 of their papers we have counts for
8 papers
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…
Context Memorization for Efficient Long Context Generation
Yasuyuki Okoshi, Hao Mark Chen, Guanxi Lu +3
Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While prefix-augmented inference is effe…
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…
AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size
Guanxi Lu, Hao Mark Chen, Yuto Karashima +3
Diffusion-based large language models (dLLMs) are gaining attention for their inherent capacity for parallel decoding, offering a compelling alternative to autoregressive LLMs. Amo…
The Strong Lottery Ticket Hypothesis for Multi-Head Attention Mechanisms
Hikari Otsuka, Daiki Chijiwa, Yasuyuki Okoshi +3
The strong lottery ticket hypothesis (SLTH) conjectures that high-performing subnetworks, called strong lottery tickets (SLTs), are hidden in randomly initialized neural networks.…
DX100: A Programmable Data Access Accelerator for Indirection
Alireza Khadem, Kamalavasan Kamalakkannan, Zhenyan Zhu +8
Indirect memory accesses frequently appear in applications where memory bandwidth is a critical bottleneck. Prior indirect memory access proposals, such as indirect prefetchers, ru…