29 citations · 30 across the 8 of their papers we have counts for
9 papers
TomasuLLM: Out-of-Order Speculative Execution for LLM Agents
Jiangnan Yu, Ceyu Xu, Mengming Li +6
Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. This observation stall presents…
A Thread-Register Decoupled GPU Execution Model for Efficient Tensor Computation
Zihan Liu, Jingwen Leng, Yangjie Zhou +12
Modern GPUs increasingly integrate Tensor Cores into the execution pipeline. Although aggregate tensor throughput continues to grow, aided by an operand supply that has evolved fro…
Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification
Chunyu Qi, Zhuoran Song, Jian Weng +6
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hin…
Affix Cache for Diffusion Large Language Models
Kaihua Liang, An Zhong, Xin Tan +4
Diffusion Large Language Models (DLLMs) enable non-autoregressive decoding, but efficient inference support remains immature: unlike autoregressive models, whose requests reuse a s…
GPU-Tile-Sim: A Tile-Centric GPU Simulation Framework for LLM Hardware-Software Co-Design
Yitong Ding, Jiawei Huang, Renyang Guan +7
Modern LLM (large language model) workloads increasingly rely on optimized GPU kernels through hardware-software co-design. These kernels achieve high-performance through fine-grai…
Cache-Resident LLM Inference in GB-Scale Last-Level Caches
Wanning Zhang, Tongzhou Gu, Marco Canini +2
Large language model (LLM) inference is increasingly dominated by data movement across the memory hierarchy. Recent 3D-stacked cache technologies have enabled GB-scale last-level c…
PIMSAB: A Processing-In-Memory System with Spatially-Aware Communication and Bit-Serial-Aware Computation
Aman Arora, Jian Weng, Siyuan Ma +2
Bit-serial Processing-In-Memory (PIM) is an attractive paradigm for accelerator architectures, for parallel workloads such as Deep Learning (DL), because of its capability to achie…
UNIT: Unifying Tensorized Instruction Compilation
Jian Weng, Animesh Jain, Jie Wang +3
Because of the increasing demand for computation in DNN, researchers develope both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approa…
Exploiting Fine-Grain Ordered Parallelism in Dense Matrix Algorithms
Jian Weng, Vidushi Dadu, Tony Nowatzki
Dense linear algebra kernels are critical for wireless applications, and the oncoming proliferation of 5G only amplifies their importance. Many such matrix algorithms are inductive…