4 citations · 5 across the 10 of their papers we have counts for
10 papers
IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference
Xintong Yang, Hao Gu, Binxing Xu +6
Large Language Models (LLMs) are increasingly expected to operate over long contexts, yet standard softmax attention incurs a KV cache that grows linearly with sequence length, qui…
Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs
Binxing Xu, Hao Gu, Lujun Li +8
Training LLMs at ultra-low precision remains a formidable challenge. Direct low-bit QAT often suffers from convergence instability and substantial training costs, exacerbated by qu…
QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training--Inference Mismatch
Hao Gu, Hao Wang, Jiacheng Liu +9
Large language model (LLM) reinforcement learning (RL) pipelines are often bottlenecked by rollout generation, making end-to-end training slow. Recent work mitigates this by runnin…
Hardware Acceleration for Neural Networks: A Comprehensive Survey
Bin Xu, Ayan Banerjee, Sandeep Gupta
Neural networks have become dominant computational workloads across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bot…
Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics
Bin Xu, Ayan Banerjee, Sandeep Gupta
Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recov…
Fast Online Digital Twinning on FPGA for Mission Critical Applications
Bin Xu, Ayan Banerjee, Sandeep K. S. Gupta
Digital twinning enables real-time simulation and predictive modeling by maintaining a continuously updated virtual representation of a physical system. In mission-critical applica…
Accelerated Digital Twin Learning for Edge AI: A Comparison of FPGA and Mobile GPU
Bin Xu, Ayan Banerjee, Midhat Urooj +1
Digital twins (DTs) can enable precision healthcare by continually learning a mathematical representation of patient-specific dynamics. However, mission critical healthcare applica…
Hardware Software Optimizations for Fast Model Recovery on Reconfigurable Architectures
Bin Xu, Ayan Banerjee, Sandeep Gupta
Model Recovery (MR) is a core primitive for physical AI and real-time digital twins, but GPUs often execute MR inefficiently due to iterative dependencies, kernel-launch overheads,…
Model Recovery at the Edge under Resource Constraints for Physical AI
Bin Xu, Ayan Banerjee, Sandeep K. S. Gupta
Model Recovery (MR) enables safe, explainable decision making in mission-critical autonomous systems (MCAS) by learning governing dynamical equations, but its deployment on edge de…
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM
Payal Kamboj, Ayan Banerjee, Bin Xu +1
Rare events, due to their infrequent occurrences, do not have much data, and hence deep learning techniques fail in estimating the distribution for such data. Open-vocabulary model…