2 citations · 3 across the 12 of their papers we have counts for
12 papers
Robust multi-hypothesis quantum-state discrimination under unknown common unitary perturbations via least favorable priors
Daichi Fujiki, Fuyuhiko Tanaka
We study robust K-ary quantum-state discrimination when all candidate states are affected by the same unknown common unitary perturbation. The unknown perturbation does not represe…
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…
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.…
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…
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…
Multi-Dimensional Vector ISA Extension for Mobile In-Cache Computing
Alireza Khadem, Daichi Fujiki, Hilbert Chen +4
In-cache computing technology transforms existing caches into long-vector compute units and offers low-cost alternatives to building expensive vector engines for mobile CPUs. Unfor…
Partially Frozen Random Networks Contain Compact Strong Lottery Tickets
Hikari Otsuka, Daiki Chijiwa, Ángel López García-Arias +6
Randomly initialized dense networks contain subnetworks that achieve high accuracy without weight learning--strong lottery tickets (SLTs). Recently, Gadhikar et al. (2023) demonstr…
HALO-CAT: A Hidden Network Processor with Activation-Localized CIM Architecture and Layer-Penetrative Tiling
Yung-Chin Chen, Shimpei Ando, Daichi Fujiki +2
To address the 'memory wall' problem in NN hardware acceleration, we introduce HALO-CAT, a software-hardware co-design optimized for Hidden Neural Network (HNN) processing. HALO-CA…
Vector-Processing for Mobile Devices: Benchmark and Analysis
Alireza Khadem, Daichi Fujiki, Nishil Talati +2
Vector processing has become commonplace in today's CPU microarchitectures. Vector instructions improve performance and energy which is crucial for resource-constraint mobile devic…
OSA-HCIM: On-The-Fly Saliency-Aware Hybrid SRAM CIM with Dynamic Precision Configuration
Yung-Chin Chen, Shimpei Ando, Daichi Fujiki +2
Computing-in-Memory (CIM) has shown great potential for enhancing efficiency and performance for deep neural networks (DNNs). However, the lack of flexibility in CIM leads to an un…